A third indeterminacy of labour power: Worker health investment and the indeterminacy of labour health.

This article identifies the health of the worker as a third source of labour power indeterminacy to be added to the indeterminacy of labour effort and the indeterminacy of labour mobility. The paper clearly differentiates worker health from effort as a distinct source of labour power indeterminacy-something that cannot be guaranteed and that varies for an individual over time. It considers the relationship between worker health as a new source of indeterminacy and the two extant sources of labour power indeterminacy, focussing on the way in which health moderates the relationship between effort and output. The paper also considers the way in which worker health investment moderates the indeterminacy of labour effort and labour mobility, independently of its impact on the health of the worker. The paper documents the potential value of worker health investment to the organisation and also considers the boundary conditions for investment in worker health.

"The machine doesn't judge": Counternarratives on surveillance among people accessing a safer opioid supply via biometric machines.

People who use illegal drugs experience routine surveillance, including in healthcare and harm reduction settings. The MySafe Project - a safer supply pilot project that dispenses prescription opioids via a biometric vending machine - exists in the Canadian province of British Columbia. The machine scans a participant's palmprint and has a built-in camera that records every machine interaction. The aim of this paper is to understand participants' experiences of surveillance, privacy, and personal security when accessing this novel program. An integrative case study and grounded theory methodology was employed. Qualitative one-to-one interviews were conducted with 46 MySafe participants across three different program sites in Vancouver. We used a team-based approach to code interview transcripts and utilized directed and conventional content analyses for deductive and inductive analyses. While participants described negative experiences of surveillance in other public and harm reduction settings, they did not have concerns regarding cameras, collection of personal information, tracking, nor staff issues associated with MySafe. Similarly, while some participants had privacy concerns in other settings, very few privacy and confidentiality concerns were expressed regarding accessing the machine in front of others. Lastly, while some participants reported being targeted by others when accessing the machines, most participants described how cameras, staff, and machine locations helped ensure a sense of safety. Despite negative experiences of surveillance and privacy issues elsewhere, participants largely lacked concern regarding the MySafe program and machines. The machine-human interaction was characterized as different than some human-human interactions as the machine is completing tasks in a manner that is acceptable and comfortable to participants, leading to a social preference toward the machines in comparison to other surveilled means of accessing medications. These findings provide an opportunity to rethink how we conceptualize surveillance, medication access, and harm reduction programs targeting people who use drugs.

Synthetically accessible de novo design using reaction vectors: Application to PARP1 inhibitors.

De novo design has been a hotly pursued topic for many years. Most recent developments have involved the use of deep learning methods for generative molecular design. Despite increasing levels of algorithmic sophistication, the design of molecules that are synthetically accessible remains a major challenge. Reaction-based de novo design takes a conceptually simpler approach and aims to address synthesisability directly by mimicking synthetic chemistry and driving structural transformations by known reactions that are applied in a stepwise manner. However, the use of a small number of hand-coded transformations restricts the chemical space that can be accessed and there are few examples in the literature where molecules and their synthetic routes have been designed and executed successfully. Here we describe the application of reaction-based de novo design to the design of synthetically accessible and biologically active compounds as proof-of-concept of our reaction vector-based software. Reaction vectors are derived automatically from known reactions and allow access to a wide region of synthetically accessible chemical space. The design was aimed at producing molecules that are active against PARP1 and which have improved brain penetration properties compared to existing PARP1 inhibitors. We synthesised a selection of the designed molecules according to the provided synthetic routes and tested them experimentally. The results demonstrate that reaction vectors can be applied to the design of novel molecules of biological relevance that are also synthetically accessible.

Disclosing Recovery: A pilot randomized controlled trial of a patient decision aid to improve disclosure processes for people in treatment for opioid use disorder.

INTRODUCTION: People engaged in treatment for opioid use disorder (OUD) report struggling with whether and how to disclose, or share information about their OUD history and/or treatment with others. Yet, disclosure can act as a gateway to re-establishing social connection and support during recovery. The current study describes a pilot randomized controlled trial of Disclosing Recovery: A Decision Aid and Toolkit, a patient decision aid designed to facilitate disclosure decisions and build disclosure skills. METHODS: Participants (n = 50) were recruited from a community-based behavioral health organization in 2021-2022 and randomized to receive the Disclosing Recovery intervention versus an attention-control comparator. They responded to surveys immediately after receiving the intervention as well as one month following the intervention at a follow-up appointment. Primary outcome analyses examined indicators of implementation of the intervention to inform a future efficacy trial. Secondary outcome analyses explored impacts of the intervention on the decision-making process, disclosure rates, and relationships. RESULTS: Participants were successfully recruited, randomized, and retained, increasing confidence in the feasibility of future efficacy trials to test the Disclosing Recovery intervention. Moreover, participants in the Disclosing Recovery intervention agreed that the intervention is acceptable, feasible, and appropriate. They additionally reported a higher quality of their decision-making process and decisions than participants in the comparator condition. At their follow-up appointment, participants with illicit opioid use who received the Disclosing Recovery intervention were less likely to disclose than those who received the comparator condition. Moreover, significant interactions between illicit opioid use and the intervention condition indicated that participants without illicit opioid use who received the Disclosing Recovery intervention reported greater closeness to and social support from their planned disclosure recipient than those who received the comparator condition. CONCLUSIONS: The Disclosing Recovery intervention appears to be an acceptable, feasible, and appropriate patient decision aid for addressing disclosure processes among people in treatment for OUD. Moreover, preliminary results suggest that it shows promise in improving relationship closeness and social support in patients without illicit opioid use. More testing is merited to determine the intervention's efficacy and effectiveness in improving relationship and treatment outcomes for people in treatment for OUD.

A Conduit for a Culturally Competent Consent: Medical Interpreters' Perspectives on Surgical Informed Consent Discussions.

OBJECTIVE: To understand medical interpreter's perspectives on surgical informed consent discussions and provide feedback for surgeons on improving these conversations. SUMMARY BACKGROUND DATA: Informed consent is a critical component of patient-centered surgical decision-making. For patients with limited English proficiency (LEP), this conversation may be less thorough, even with a medical interpreter, leaving patients with an inadequate understanding of their diagnosis or treatment options. METHODS: A semi-structured interview guide was developed with input from interpreters and a qualitative research expert. We purposively sampled medical interpreters representing multiple languages until thematic saturation was achieved. Participants discussed their experience with the surgical consent discussion and process. Interview transcripts were analyzed using emergent thematic analysis. RESULTS: Among 22 interpreters, there were ten languages represented and an average experience of 15 years (range 4-40 y). Four major themes were identified. First, interpreters consistently described their roles as patient advocates and cultural brokers. Second, interpreters reported unique patient attributes that influence the discussion, often based on patients' cultural values/expectations, anticipated decisional autonomy, and family support. Third, interpreters emphasized the importance of surgeons demonstrating compassion and patience, using simple terminology, conversing around the consent, providing context about the form/process, and initiating a pre-encounter discussion. Finally, interpreters suggested reducing legal terminology on consent forms and translation into other languages. CONCLUSIONS: Experienced interpreters highlighted multiple factors associated with effective and culturally tailored informed consent discussions. Surgeons should recognize interpreters' critical and complex roles, be cognizant of cultural variations among patients with LEP, and improve interpersonal and communication skills to facilitate effective understanding.

Protocol for a pragmatic cluster randomised controlled trial to evaluate the effectiveness of digital health interventions in improving non-communicable disease management during the pandemic in rural Pakistan.

BACKGROUND: The COVID-19 pandemic has revealed gaps in global health systems, especially in the low- and middle-income countries (LMICs). Evidence shows that patients with non-communicable diseases (NCDs) are at higher risk of contracting COVID-19 and suffering direct and indirect health consequences. Considering the future challenges such as environmental disasters and pandemics to the LMICs health systems, digital health interventions (DHI) are well poised to strengthen health care resilience. This study aims to implement and evaluate a comprehensive package of DHIs of integrated COVID-NCD care to manage NCDs in primary care facilities in rural Pakistan. METHODS: The study is designed as a pragmatic, parallel two-arm, multi-centre, mix-methods cluster randomised controlled trial. We will randomise 30 primary care facilities in three districts of Punjab, where basic hypertension and diabetes diagnosis and treatment are provided, with a ratio of 1:1 between intervention and control. In each facility, we will recruit 50 patients who have uncontrolled hypertension. The intervention arm will receive training on an integrated COVID-NCD guideline, and will use a smartphone app-based telemedicine platform where patients can communicate with health providers and peer-supporters, along with a remote training and supervision system. Usual care will be provided in the control arm. Patients will be followed up for 10 months. Our primary indicator is systolic blood pressure measured at 10 months. A process evaluation guided by implementation science frameworks will be conducted to explore implementation questions. A cost-effectiveness evaluation will be conducted to inform future scale up in Pakistan and other LMICs. DISCUSSION: Our study is one of the first randomised controlled trials to evaluate the effectiveness of DHIs to manage NCDs to strengthen health system resilience in LMICs. We will also evaluate the implementation process and cost-effectiveness to inform future scale-up in similar resource constrained settings. TRIAL REGISTRATION: ClinicalTrials.gov Identifier-NCT05699369.

Surveillance is the first step to preventing injury among fast jet aircrew: results of a 2-year prospective cohort study.

OBJECTIVES: Injury surveillance is imperative for injury prevention but difficult in military populations. Our objective was to accurately describe the pattern of musculoskeletal complaints among Royal Australian Air Force (RAAF) fast jet aircrew (FJA) using the validated University of Canberra Fast Jet Aircrew Musculoskeletal Questionnaire (UC-FJAMQ) over a 2-year period, and determine injury burden on the workforce and operational capability. METHODS: 306 RAAF FJA were monitored over a 2-year period (4×5 month reporting periods). Musculoskeletal complaint episodes (MCEs) were captured weekly using the UC-FJAMQ. Time loss episodes (TLEs) were captured from the UC-FJAMQ and injury registers completed by embedded physiotherapists. Cumulative severity and operational impact scores from the UC-FJAMQ, and time loss duration, were used to describe severity and calculate burden. RESULTS: Mean weekly UC-FJAMQ response rate was 62%. 1012 MCEs were captured, with a mean weekly prevalence of 14.9% (95% CI 14.2-15.6), and incidence of 4.1 episodes per person-year (95% CI 3.9-4.4). A total of 145 TLEs were captured, with a mean 5-month prevalence of 12.4% (range 8.9-15.3%), and incidence of 0.37 episodes per person-year (95% CI 0.31-0.43). Spinal regions accounted for 81% of MCEs and contributed 76% and 85% of burden in relation to cumulative severity and operational impact, respectively. 57% of TLE burden came from spinal regions. CONCLUSIONS: Despite modest weekly UC-FJAMQ response rates, musculoskeletal complaints were shown to be widespread and negatively impact operational capability. Future injury prevention efforts among FJA should focus on spinal regions, particularly the neck.

What does high value care for musculoskeletal conditions mean and how do you apply it in practice? A consensus statement from a research network of physiotherapists in New South Wales, Australia.

OBJECTIVES: To develop a physiotherapist-led consensus statement on the definition and provision of high-value care for people with musculoskeletal conditions. DESIGN: We performed a three-stage study using Research And Development/University of California Los Angeles Appropriateness Method methodology. We reviewed evidence about current definitions through a rapid literature review and then performed a survey and interviews with network members to gather consensus. Consensus was finalised in a face-to-face meeting. SETTING: Australian primary care. PARTICIPANTS: Registered physiotherapists who are members of a practice-based research network (n=31). RESULTS: The rapid review revealed two definitions, four domains of high value care and seven themes of high-quality care. Online survey responses (n=26) and interviews (n=9) generated two additional high-quality care themes, a definition of low-value care, and 21 statements on the application of high value care. Consensus was reached for three working definitions (high value, high-quality and low value care), a final model of four high value care domains (high-quality care, patient values, cost-effectiveness, reducing waste), nine high-quality care themes and 15 statements on application. CONCLUSION: High value care for musculoskeletal conditions delivers most value for the patient, and the clinical benefits outweigh the costs to the individual or system providing the care. High-quality care is evidence based, effective and safe care that is patient-centred, consistent, accountable, timely, equitable and allows easy interaction with healthcare providers and healthcare systems.

Optimizing combat readiness for military surgeons without trauma fellowship training: Engaging the "voluntary faculty" model.

BACKGROUND: Appropriate operative volume remains a critical component in mitigating surgical atrophy and maintaining clinical competency. The initiation of military-civilian surgical partnerships (MCPs) has been proposed for addressing knowledge, skills, and abilities (KSA) metrics to address concerns over operational readiness and the low acuity experienced by military surgeons. This study investigates the first partnership for Navy surgical staff at a nonacademic Military Treatment Facility (MTF) with a regional academic Army Military Treatment Facility (AMTF) and a civilian, nonacademic level II trauma center devised to improve operational readiness for attending surgeons. We hypothesize that a skill sustainment MCP will allow military surgeons to meet combat readiness standards as measured by the KSA metric. METHODS: A memorandum of understanding was initiated between the Navy Military Treatment Facility (NMTF), the AMTF, and the level II civilian trauma center (CTC). The single military surgeon in this study was classified as "voluntary faculty" at the CTC. Total case volume and acuity were recorded over an 11-month period. Knowledge, skills, and abilities metrics were calculated using the standard national provider identifier number and the novel case-log based method. RESULTS: A total of 156 cases were completed by a single surgeon over the study period, averaging 52 cases per institution. Significantly more KSAs were obtained at the CTC compared with NMTF (5,954 vs. 2,707; p < 0.001). Significantly more emergent cases were observed at the CTC compared with the MTFs (χ 2 = 7.1, n = 96, p < 0.05). At a single site, AMTF, a significant difference in the calculated KSA score, was observed between the national provider identifier and case-log methods (5,278 vs. 3,297; p = 0.04). CONCLUSION: The skill sustainment MCP between NMTF and CTC increased surgical readiness and exposed surgeons to increased operative acuity. The voluntary faculty model reduces direct litigation exposure and encourages clinical competency for military surgeons while remaining a deployable asset to the global military effort. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level IV.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.

Background: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life and there remain no validated predictive models to guide its use. Methods: Digital pathology image and clinical data from pre-treatment prostate tissue from 5,727 patients enrolled on five phase III randomized trials treated with radiotherapy +/- ADT were used to develop and validate an artificial intelligence (AI)-derived predictive model to assess ADT benefit with the primary endpoint of distant metastasis. After the model was locked, validation was performed on NRG/RTOG 9408 (n = 1,594) that randomized men to radiotherapy +/- 4 months of ADT. Fine-Gray regression and restricted mean survival times were used to assess the interaction between treatment and predictive model and within predictive model positive and negative subgroup treatment effects. Results: In the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis (subdistribution hazard ratio [sHR] = 0.64, 95%CI [0.45-0.90], p = 0.01). The predictive model-treatment interaction was significant (p-interaction = 0.01). In predictive model positive patients (n = 543, 34%), ADT significantly reduced the risk of distant metastasis compared to radiotherapy alone (sHR = 0.34, 95%CI [0.19-0.63], p < 0.001). There were no significant differences between treatment arms in the predictive model negative subgroup (n = 1,051, 66%; sHR = 0.92, 95%CI [0.59-1.43], p = 0.71). Conclusions: Our data, derived and validated from completed randomized phase III trials, show that an AI-based predictive model was able to identify prostate cancer patients, with predominately intermediate-risk disease, who are likely to benefit from short-term ADT.

A dynamic in vitro developing testis model reflects structures and functions of testicular development in vivo.

To better define appropriate applications of our 3-dimensional testicular co-culture as a model for reproductive toxicology, we evaluated the ability of the model to capture structural and functional elements that can be targeted by reproductive toxicants. Testicular co-cultures were prepared from postnatal day 5 male rats and cultured with a Matrigel overlay. Following a 2-day acclimation period, we characterized functional pathway dynamics by evaluating morphology, protein expression, testosterone concentrations, and global gene expression at a range of timepoints from experimental days 0-21. Western blotting confirmed expression of Sertoli cell, Leydig cell, and spermatogonial cell-specific protein markers. Testosterone detected in cell culture media indicates active testosterone production. Quantitative pathway analysis identified Gene Ontology biological processes enriched among genes significantly changing over the course of 21 days. Processes enriched among genes significantly increasing through time include general developmental processes (morphogenesis, tissue remodeling, etc.), steroid regulation, Sertoli cell development, immune response, and stress and apoptosis. Processes enriched among genes significantly decreasing over time include several related to male reproductive development (seminiferous tubule development, male gonad development, Leydig cell differentiation, Sertoli cell differentiation), all of which appear to peak in expression between days 1 and 5 before decreasing at later timepoints. This analysis provides a temporal roadmap for specific biological process of interest for reproductive toxicology in the model and anchors the model to sensitive phases of in vivo development, helping to define the relevance of the model for in vivo processes.

Alteration of oral microbiome composition in children living with pesticide-exposed farm workers.

Our prior work shows that azinphos-methyl pesticide exposure is associated with altered oral microbiomes in exposed farmworkers. Here we extend this analysis to show the same association pattern is also evident in their children. Oral buccal swab samples were analyzed at two time points, the apple thinning season in spring-summer 2005 for 78 children and 101 adults and the non-spray season in winter 2006 for 62 children and 82 adults. The pesticide exposure for the children were defined by the farmworker occupation of the cohabitating household adult and the blood azinphos-methyl detection of the cohabitating adult. Oral buccal swab 16S rRNA sequencing determined taxonomic microbiota proportional composition from concurrent samples from both adults and children. Analysis of the identified bacteria showed significant proportional changes for 12 of 23 common oral microbiome genera in association with azinphos-methyl detection and farmworker occupation. The most common significantly altered genera had reductions in the abundance of Streptococcus, suggesting an anti-microbial effect of the pesticide. Principal component analysis of the microbiome identified two primary clusters, with association of principal component 1 to azinphos-methyl blood detection and farmworker occupational status of the household. The children's buccal microbiota composition clustered with their household adult in ∼95% of the households. Household adult farmworker occupation and household pesticide exposure is associated with significant alterations in their children's oral microbiome composition. This suggests that parental occupational exposure and pesticide take-home exposure pathways elicit alteration of their children's microbiomes.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.

BACKGROUND: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use. METHODS: We used digital pathology images from pretreatment prostate tissue and clinical data from 5727 patients enrolled in five phase 3 randomized trials, in which treatment was radiotherapy with or without ADT, as our data source to develop and validate an artificial intelligence (AI)­derived predictive patient-specific model that would determine which patients would develop the primary end point of distant metastasis. The model used baseline data to provide a binary output that a given patient will likely benefit from ADT or not. After the model was locked, validation was performed using data from NRG Oncology/Radiation Therapy Oncology Group (RTOG) 9408 (n=1594), a trial that randomly assigned men to radiotherapy plus or minus 4 months of ADT. Fine­Gray regression and restricted mean survival times were used to assess the interaction between treatment and the predictive model and within predictive model­positive, i.e., benefited from ADT, and ­negative subgroup treatment effects. RESULTS: Overall, in the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis. Of these enrolled patients, 543 (34%) were model positive, and ADT significantly reduced the risk of distant metastasis compared with radiotherapy alone. Of 1051 patients who were model negative, ADT did not provide benefit. CONCLUSIONS: Our AI-based predictive model was able to identify patients with a predominantly intermediate risk for prostate cancer likely to benefit from short-term ADT. (Supported by a grant [U10CA180822] from NRG Oncology Statistical and Data Management Center, a grant [UG1CA189867] from NCI Community Oncology Research Program, a grant [U10CA180868] from NRG Oncology Operations, and a grant [U24CA196067] from NRG Specimen Bank from the National Cancer Institute and by Artera, Inc. ClinicalTrials.gov numbers NCT00767286, NCT00002597, NCT00769548, NCT00005044, and NCT00033631.)

A Validated Injury Surveillance and Monitoring Tool for Fast Jet Aircrew: Translating Sports Medicine Paradigms to a Military Population.

BACKGROUND: Military populations, including fast jet aircrew (FJA - aka fighter aircrew/pilots), commonly suffer from musculoskeletal complaints, which reduce performance and operational capability. Valid surveillance tools and agreed recordable injury definitions are lacking. Our objective was to develop and then evaluate the validity of a musculoskeletal complaints surveillance and monitoring tool for FJA. METHODS: A Delphi study with international experts sought consensus on recordable injury definitions and important content for use in a surveillance and monitoring tool for FJA. Using these results and feedback from end-users (FJA), the University of Canberra Fast Jet Aircrew Musculoskeletal Questionnaire (UC-FJAMQ) was developed. Following its use with 306 Royal Australian Air Force (RAAF) FJA over 4 × five-month reporting periods, validity of the UC-FJAMQ was evaluated via multi-level factor analysis (MFA) and compared with routine methods of injury surveillance. RESULTS: Consensus was achieved for: eight words/descriptors for defining a musculoskeletal complaint; six definitions of recordable injury; and 14 domains important for determining overall severity. The UC-FJAMQ was developed and refined. MFA identified three distinct dimensions within the 11 items used to determine severity: operational capability, symptoms, and care-seeking. MFA further highlighted that symptom severity and seeking medical attention were poor indicators of the impact musculoskeletal complaints have upon operational capability. One hundred and fifty-two episodes of time loss were identified, with the UC-FJAMQ identifying 79% of these, while routine methods identified 49%. Despite modest weekly reporting rates (61%), the UC-FJAMQ outperformed routine surveillance methods. CONCLUSIONS: The UC-FJAMQ was developed to specifically address the complexities of injury surveillance with FJA, which are similar to those noted in other military and sporting populations. The results demonstrated the UC-FJAMQ to be sensitive and valid within a large group of FJA over 4 × five-month reporting periods. Adoption of consistent, sensitive, and valid surveillance methods will strengthen the FJA injury prevention literature, ultimately enhancing their health, performance, and operational capability.

Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials.

Prostate cancer is the most frequent cancer in men and a leading cause of cancer death. Determining a patient's optimal therapy is a challenge, where oncologists must select a therapy with the highest likelihood of success and the lowest likelihood of toxicity. International standards for prognostication rely on non-specific and semi-quantitative tools, commonly leading to over- and under-treatment. Tissue-based molecular biomarkers have attempted to address this, but most have limited validation in prospective randomized trials and expensive processing costs, posing substantial barriers to widespread adoption. There remains a significant need for accurate and scalable tools to support therapy personalization. Here we demonstrate prostate cancer therapy personalization by predicting long-term, clinically relevant outcomes using a multimodal deep learning architecture and train models using clinical data and digital histopathology from prostate biopsies. We train and validate models using five phase III randomized trials conducted across hundreds of clinical centers. Histopathological data was available for 5654 of 7764 randomized patients (71%) with a median follow-up of 11.4 years. Compared to the most common risk-stratification tool-risk groups developed by the National Cancer Center Network (NCCN)-our models have superior discriminatory performance across all endpoints, ranging from 9.2% to 14.6% relative improvement in a held-out validation set. This artificial intelligence-based tool improves prognostication over standard tools and allows oncologists to computationally predict the likeliest outcomes of specific patients to determine optimal treatment. Outfitted with digital scanners and internet access, any clinic could offer such capabilities, enabling global access to therapy personalization.

RENATE: A Pseudo-retrosynthetic Tool for Synthetically Accessible de novo Design.

Reaction-based de novo design refers to the generation of synthetically accessible molecules using transformation rules extracted from known reactions in the literature. In this context, we have previously described the extraction of reaction vectors from a reactions database and their coupling with a structure generation algorithm for the generation of novel molecules from a starting material. An issue when designing molecules from a starting material is the combinatorial explosion of possible product molecules that can be generated, especially for multistep syntheses. Here, we present the development of RENATE, a reaction-based de novo design tool, which is based on a pseudo-retrosynthetic fragmentation of a reference ligand and an inside-out approach to de novo design. The reference ligand is fragmented; each fragment is used to search for similar fragments as building blocks; the building blocks are combined into products using reaction vectors; and a synthetic route is suggested for each product molecule. The RENATE methodology is presented followed by a retrospective validation to recreate a set of approved drugs. Results show that RENATE can generate very similar or even identical structures to the corresponding input drugs, hence validating the fragmentation, search, and design heuristics implemented in the tool.

Trends in Surgical Case Volume During Pacific Partnership Missions Onboard USNS Mercy.

INTRODUCTION: Since 2006, the U.S. Navy has conducted six Pacific Partnership (PP) missions throughout Southeast Asia on board the U.S. Naval Ship Mercy (T-AH 19). This study describes trends in overall and surgical specialty operative volumes to better understand the burden of surgical disease treated during these humanitarian and civic assistance (HCA) operations. This information can assist medical planners and surgical leaders involved in future humanitarian missions. MATERIALS AND METHODS: Following approval from the Naval Medical Center San Diego Institutional Review Board, a retrospective review of surgical case data was performed for the six PP missions from 2006 to 2018. Data collected included patient demographics, Current Procedural Terminology codes, and surgical specialty. The primary outcome was surgical case volume per specialty. Secondary outcomes included surgical staffing per mission and overall trends in operative volume. RESULTS: A total of 3,826 operative procedures were performed during the study period. Mission years in which case volume for both general surgery and ophthalmology were below their respective medians were associated with the least total surgical services to host nations (HNs). The number of active duty Navy surgeons varied with each mission; however, the staffing for a PP mission generally included at least two general surgeons, one ophthalmologist, one plastic surgeon, one pediatric surgeon, one orthopedic surgeon, one otolaryngologist, one oral surgeon, one urologist, and one obstetrician-gynecologist. Case volume per surgeon was highest in 2006 (50 cases per surgeon) and decreased after 2006, reaching an all-time low during the 2018 PP mission (10 cases per surgeon). Pediatric surgery and plastic surgery had the highest average case volumes per surgeon at 58 and 46 cases per surgeon, respectively, while oromaxillofacial surgery and neurosurgery had the lowest average case volumes per surgeon at 9 and 14 cases per surgeon, respectively. CONCLUSIONS: Operative volume on military HCA missions is greatly influenced by the priorities of the HN, the mission focus, the number of individuals from the HN that present for screening, and the availability of personnel and resources available on the hospital ship. Future mission planning should optimize general surgery and ophthalmology staffing and essential equipment, as total mission case volumes were highly dependent upon the productivity of these two specialties. Careful determination of the surgical needs of HNs should serve as a guide for the selection of subspecialists to maximize effectiveness in future military HCA missions.

Latent classes associated with the intention to use a symptom checker for self-triage.

It is currently unknown which attitude-based profiles are associated with symptom checker use for self-triage. We sought to identify, among university students, attitude-based latent classes (population profiles) and the association between latent classes with the future use of symptom checkers for self-triage. Informed by the Technology Acceptance Model and a larger mixed methods study, a cross-sectional survey was developed and administered to students (aged between 18 and 34 years of age) at a University in Ontario. Latent class analysis (LCA) was used to identify attitude-based profiles that exist among the sample while general linear modeling was applied to identify the association between latent classes and future symptom checker use for self-triage. Of the 1,547 students who opened the survey link, 1,365 did not use a symptom checker in the past year and were thus identified as "non-users". After removing missing data (remaining sample = n = 1,305), LCA revealed five attitude-based profiles: tech acceptors, tech rejectors, skeptics, tech seekers, and unsure acceptors. Tech acceptors and tech rejectors were the most and least prevalent classes, respectively. As compared to tech rejectors, tech seekers and unsure acceptors were the latent classes with the highest and lowest odds of future symptom checker use, respectively. After controlling for confounders, the effect of latent classes on symptom checker use remains significant (p-value < .0001) with the odds of future use in tech acceptors being 5.6 times higher than the odds of future symptom checker use in tech rejectors [CI: (3.458, 9.078); p-value < .0001]. Attitudes towards AI and symptom checker functionality result in different population profiles that have different odds of using symptom checkers for self-triage. Identifying a person's or group's membership to a population profile could help in developing and delivering tailored interventions aimed at maximizing use of validated symptom checkers.