Evaluation of adherence to STARD for Abstracts in a diverse sample of diagnostic accuracy abstracts published in 2012 and 2019 reveals suboptimal reporting practices.

OBJECTIVE: To evaluate the completeness of reporting in a sample of abstracts on diagnostic accuracy studies before and after the release of STARD for Abstracts in 2017. METHODS: We included 278 diagnostic accuracy abstracts published in 2012 (N=138) and 2019 (N=140) and indexed in EMBASE. We analyzed their adherence to 10 items of the 11-item STARD for Abstracts checklist and explored variability in reporting across abstract characteristics using multivariable Poisson modeling. RESULTS: Most of the 278 abstracts (75%) were published in discipline-specific journals, with a median impact factor of 2.9 (IQR: 1.9-3.7). The majority (41%) of abstracts reported on imaging tests. Overall, a mean of 5.4/10 (SD: 1.4) STARD for Abstracts items was reported (range: 1.2-9.7). Items reported in less than one-third of abstracts included 'eligible patient demographics' (24%), 'setting of recruitment' (30%), 'method of enrolment' (18%), 'estimates of precision for accuracy measures' (26%), and 'protocol registration details' (4%). We observed substantial variability in reporting across several abstract characteristics, with higher adherence associated with the use of a structured abstract, no journal limit for abstract word count, abstract word count above the median, one-gate enrolment design, and prospective data collection. There was no evidence of an increase in the number of reported items between 2012 and 2019 (5.2 vs. 5.5 items; adjusted reporting ratio 1.04 [95%CI: 0.98-1.10]). CONCLUSION: This sample of diagnostic accuracy abstracts revealed suboptimal reporting practices, without improvement between 2012 and 2019. The test evaluation field could benefit from targeted knowledge translation strategies to improve completeness of reporting in abstracts.

Adrenal Mass Biopsy in Patients Without Extraadrenal Primary Malignancy: A Multicenter Study.

BACKGROUND. Adrenal washout CT is not useful for evaluating incidental adrenal masses in patients without known or suspected primary extraadrenal malignancy. OBJECTIVE. The purpose of our study was to evaluate the diagnostic utility of adrenal mass biopsy in patients without known or suspected extraadrenal primary malignancy. METHODS. This retrospective six-center study included 69 patients (mean age, 56 years; 32 men, 37 women) without known or suspected extraadrenal primary malignancy who underwent image-guided core needle biopsy between January 2004 and June 2021 of a mass suspected to be arising from the adrenal gland. Biopsy results were classified as diagnostic or nondiagnostic. For masses resected after biopsy, histopathologic concordance was assessed between diagnoses from biopsy and resection. Masses were classified as benign or malignant by resection or imaging follow-up, and all nondi-agnostic biopsies were classified as false results. RESULTS. The median mass size was 7.4 cm (range, 1.9-19.2 cm). Adrenal mass biopsy had a diagnostic yield of 64% (44/69; 95% CI, 51-75%). After biopsy, 25 masses were resected, and 44 had imaging follow-up. Of the masses that were resected after diagnostic biopsy, diagnosis was concordant between biopsy and resection in 100% (12/12). Of the 13 masses that were resected after nondiagnostic biopsy, the diagnosis from re-section was benign in eight masses and malignant in five masses. The 44 masses with imaging follow-up included one mass with diagnostic biopsy yielding benign adenoma and two masses with nondiagnostic biopsy results that were classified as malignant by imaging follow-up. Biopsy had overall sensitivity and specificity for malignancy of 73% (22/30) and 54% (21/39), respectively; diagnostic biopsies had sensitivity and specificity for malignancy of 96% (22/23) and 100% (21/21), respectively. Among nine nondi-agnostic biopsies reported as adrenocortical neoplasm, six were classified as malignant by the reference standard (resection showing adrenocortical carcinoma in four, resection showing adrenocortical neoplasm of uncertain malignant potential in one, imaging follow-up consistent with malignancy in one). CONCLUSION. Adrenal mass biopsy had low diagnostic yield, with low sensitivity and low specificity for malignancy. A biopsy result of adrenocortical neoplasm did not reliably differentiate benign and malignant adrenal masses. CLINICAL IMPACT. Biopsy appears to have limited utility for the evaluation of incidental adrenal masses in patients without primary extraadrenal malignancy.

Technological Intervention to Improve Alarm Management in Acute Care Telemetry Units.

Background: Telemetry monitoring is intended to improve patient safety and reduce harm. However, excessive monitor alarms may have the undesired effect of staff ignoring, silencing, or delaying a response due to alarm fatigue. Outlier patients, or those patients who are responsible for generating the most monitor alarms, contribute to excessive monitor alarms. Methods: Daily alarm data reports at a large academic medical center indicated that one or two patient outliers generated the most alarms daily. A technological intervention aimed at reminding registered nurses (RNs) to adjust alarm thresholds for patients who triggered excessive alarms was implemented. The notification was sent to the assigned RN's mobile phone when a patient exceeded the unit's seven-day average of alarms per day by greater than 400%. Results: A reduction in average alarm duration was observed across the four acute care telemetry units (P < 0.001), with an overall decrease of 8.07 seconds in the postintervention versus preintervention period. However, alarm frequency increased significantly (χ23 = 34.83, P < 0.001). Conclusion: Implementing a technological intervention to notify RNs to adjust alarm parameters may reduce alarm duration. Reducing alarm duration may improve RN telemetry management, alarm fatigue, and awareness. More research is needed to support this conclusion, as well as to determine the cause of the observed increase in alarm frequency.

How to Critically Appraise and Interpret Systematic Reviews and Meta-Analyses of Diagnostic Accuracy: A User Guide.

Systematic reviews of diagnostic accuracy studies can provide the best available evidence to inform decisions regarding the use of a diagnostic test. In this guide, the authors provide a practical approach for clinicians to appraise diagnostic accuracy systematic reviews and apply their results to patient care. The first step is to identify an appropriate systematic review with a research question matching the clinical scenario. The user should evaluate the rigor of the review methods to evaluate its credibility (Did the review use clearly defined eligibility criteria, a comprehensive search strategy, structured data collection, risk of bias and applicability appraisal, and appropriate meta-analysis methods?). If the review is credible, the next step is to decide whether the diagnostic performance is adequate for clinical use (Do sensitivity and specificity estimates exceed the threshold that makes them useful in clinical practice? Are these estimates sufficiently precise? Is variability in the estimates of diagnostic accuracy across studies explained?). Diagnostic accuracy systematic reviews that are judged to be credible and provide diagnostic accuracy estimates with sufficient certainty and relevance are the most useful to inform patient care. This review discusses comparative, noncomparative, and emerging approaches to systematic reviews of diagnostic accuracy using a clinical scenario and examples based on recent publications.

Is There Evidence of P-Hacking in Imaging Research?

BACKGROUND: P-hacking, the tendency to run selective analyses until they become significant, is prevalent in many scientific disciplines. PURPOSE: This study aims to assess if p-hacking exists in imaging research. METHODS: Protocol, data, and code available here https://osf.io/xz9ku/?view_only=a9f7c2d841684cb7a3616f567db273fa. We searched imaging journals Ovid MEDLINE from 1972 to 2021. Text mining using Python script was used to collect metadata: journal, publication year, title, abstract, and P-values from abstracts. One P-value was randomly sampled per abstract. We assessed for evidence of p-hacking using a p-curve, by evaluating for a concentration of P-values just below .05. We conducted a one-tailed binomial test (α = .05 level of significance) to assess whether there were more P-values falling in the upper range (e.g., .045 < P < .05) than in the lower range (e.g., .04 < P < .045). To assess variation in results introduced by our random sampling of a single P-value per abstract, we repeated the random sampling process 1000 times and pooled results across the samples. Analysis was done (divided into 10-year periods) to determine if p-hacking practices evolved over time. RESULTS: Our search of 136 journals identified 967,981 abstracts. Text mining identified 293,687 P-values, and a total of 4105 randomly sampled P-values were included in the p-hacking analysis. The number of journals and abstracts that were included in the analysis as a fraction and percentage of the total number was, respectively, 108/136 (80%) and 4105/967,981 (.4%). P-values did not concentrate just under .05; in fact, there were more P-values falling in the lower range (e.g., .04 < P < .045) than falling just below .05 (e.g., .045 < P < .05), indicating lack of evidence for p-hacking. Time trend analysis did not identify p-hacking in any of the five 10-year periods. CONCLUSION: We did not identify evidence of p-hacking in abstracts published in over 100 imaging journals since 1972. These analyses cannot detect all forms of p-hacking, and other forms of bias may exist in imaging research such as publication bias and selective outcome reporting.

Diagnostic Accuracy of MRI for Solid Renal Masses: A Systematic Review and Meta-analysis.

BACKGROUND: Biparametric (bp)-MRI and multiparametric (mp)-MRI may improve the diagnostic accuracy of renal mass histology. PURPOSE: To evaluate the available evidence on the diagnostic accuracy of bp-MRI and mp-MRI for solid renal masses in differentiating malignant from benign, aggressive from indolent, and clear cell renal cell carcinoma (ccRCC) from other histology. STUDY TYPE: Systematic review. POPULATION: MEDLINE, EMBASE, and CENTRAL up to January 11, 2022 were searched. FIELD STRENGTH/SEQUENCE: 1.5 or 3 Tesla. ASSESSMENT: Eligible studies evaluated the accuracy of MRI (with at least two sequences: T2, T1, dynamic contrast and diffusion-weighted imaging) for diagnosis of solid renal masses in adult patients, using histology as reference standard. Risk of bias and applicability were assessed using QUADAS-2. STATISTICAL TESTS: Meta-analysis using a bivariate logitnormal random effects model. RESULTS: We included 10 studies (1239 masses from approximately 1200 patients). The risk of bias was high in three studies, unclear in five studies and low in two studies. The diagnostic accuracy of malignant (vs. benign) masses was assessed in five studies (64% [179/281] malignant). The summary estimate of sensitivity was 95% (95% confidence interval [CI]: 77%-99%), and specificity was 63% (95% CI: 46%-77%). No study assessed aggressive (vs. indolent) masses. The diagnostic accuracy of ccRCC (vs. other subtypes) was evaluated in six studies (47% [455/971] ccRCC): the summary estimate of sensitivity was 85% (95% CI: 77%-90%) and specificity was 77% (95% CI: 73%-81%). DATA CONCLUSION: Our study reveals deficits in the available evidence on MRI for diagnosis of renal mass histology. The number of studies was limited, at unclear/high risk of bias, with heterogeneous definitions of solid masses, imaging techniques, diagnostic criteria, and outcome measures. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Novel bioreactor internals for the cultivation of spore-forming fungi in pellet form.

This study introduced an automated long-term fermentation process for fungals grown in pellet form. The goal was to reduce the overgrowth of bioreactor internals and sensors while better rheological properties in the fermentation broth, such as oxygen transfer and mixing time, can be achieved. Because this could not be accomplished with continuous culture and fed-batch fermentation, repeated-batch fermentation was implemented with the help of additional bioreactor internals ("sporulation supports"). This should capture some biomass during fermentation. After harvesting the suspended biomass, intermediate cleaning was performed using a cleaning device. The biomass retained on the sporulation support went through the sporulation phase. The spores were subsequently used as inocula for the next batch. The reason for this approach was that the retained pellets could otherwise cause problems (e.g., overgrowth on sensors) in subsequent batches because the fungus would then show undesirable hyphal growth. Various sporulation supports were tested for sufficient biomass fixation to start the next batch. A reproducible spore concentration within the range of the requirements could be achieved by adjusting the sporulation support (design and construction material), and an intermediate cleaning adapted to this.

Subarachnoid hemorrhage diagnosed by lumbar puncture after negative computed tomography angiography head.

Acute headache is a common emergency department (ED) chief complaint that usually has a benign course. Rare etiologies such as subarachnoid hemorrhage (SAH) can lead to extensive disability or even death. If suspected, SAH requires an intricate and intensive diagnostic investigation. Classic teaching recommends computed tomography head imaging without contrast which, if negative, is followed by lumbar puncture (LP) to rule out SAH. With improvements in computed tomography (CT), practice patterns have begun to adjust to allow computed tomography angiography (CTA) to rule out SAH. This case report describes a 23-year-old woman presenting with headache, neck, and back pain. Her initial CT head and CTA head imaging was negative for SAH. However, 3 days later upon re-presentation to the ED with the same symptoms, an LP was positive for increasing red blood cell count in subsequent tubes. She was transferred to a facility with interventional neurology capabilities where digital subtraction angiography showed a left anterior choroidal saccular aneurysm for which she underwent coiling. Given recent changes in SAH clinical practice guidelines, this case highlights the importance of understanding the current limitations of CT imaging, understanding the risks and benefits of both CT and LP, and always maintaining a high suspicion for especially lethal and disabling conditions such as SAH.

In silico proof of principle of machine learning-based antibody design at unconstrained scale.

Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody-binding parameters. The simulation framework enables the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies.

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

Evaluating the Impact of Peer Review on the Completeness of Reporting in Imaging Diagnostic Test Accuracy Research.

BACKGROUND: Despite the nearly ubiquitous reported use of peer review among reputable medical journals, there is limited evidence to support the use of peer review to improve the quality of biomedical research and in particular, imaging diagnostic test accuracy (DTA) research. PURPOSE: To evaluate whether peer review of DTA studies published by imaging journals is associated with changes in completeness of reporting, transparency for risk of bias assessment, and spin. STUDY TYPE: Retrospective cross-sectional study. STUDY SAMPLE: Cross-sectional study of articles published in Journal of Magnetic Resonance Imaging (JMRI), Canadian Association of Radiologists Journal (CARJ), and European Radiology (EuRad) before March 31, 2020. ASSESSMENT: Initial submitted and final versions of manuscripts were evaluated for completeness of reporting using the Standards for Reporting Diagnostic Accuracy Studies (STARD) 2015 and STARD for Abstracts guidelines, transparency of reporting for risk of bias assessment based on Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2), and actual and potential spin using modified published criteria. STATISTICAL TESTS: Two-tailed paired t-tests and paired Wilcoxon signed-rank tests were used for comparisons. A P value <0.05 was considered to be statistically significant. RESULTS: We included 84 diagnostic accuracy studies accepted by three journals between 2014 and 2020 (JMRI = 30, CARJ = 23, and EuRad = 31) of the 692 which were screened. Completeness of reporting according to STARD 2015 increased significantly between initial submissions and final accepted versions (average reported items: 16.67 vs. 17.47, change of 0.80 [95% confidence interval 0.25-1.17]). No significant difference was found for the reporting of STARD for Abstracts (5.28 vs. 5.25, change of -0.03 [-0.15 to 0.11], P = 0.74), QUADAS-2 (6.08 vs. 6.11, change of 0.03 [-1.00 to 0.50], P = 0.92), actual "spin" (2.36 vs. 2.40, change of 0.04 [0.00 to 1.00], P = 0.39) or potential "spin" (2.93 vs. 2.81, change of -0.12 [-1.00 to 0.00], P = 0.23) practices. CONCLUSION: Peer review is associated with a marginal improvement in completeness of reporting in published imaging DTA studies, but not with improvement in transparency for risk of bias assessment or reduction in spin. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1.

Association of Accuracy, Conclusions, and Reporting Completeness With Acceptance by Radiology Conferences and Journals.

BACKGROUND: Preferential publication of studies with positive findings can lead to overestimation of diagnostic test accuracy (i.e. publication bias). Understanding the contribution of the editorial process to publication bias could inform interventions to optimize the evidence guiding clinical decisions. PURPOSE/HYPOTHESIS: To evaluate whether accuracy estimates, abstract conclusion positivity, and completeness of abstract reporting are associated with acceptance to radiology conferences and journals. STUDY TYPE: Meta-research. POPULATION: Abstracts submitted to radiology conferences (European Society of Gastrointestinal and Abdominal Radiology (ESGAR) and International Society for Magnetic Resonance in Medicine (ISMRM)) from 2008 to 2018 and manuscripts submitted to radiology journals (Radiology, Journal of Magnetic Resonance Imaging [JMRI]) from 2017 to 2018. Primary clinical studies evaluating sensitivity and specificity of a diagnostic imaging test in humans with available editorial decisions were included. ASSESSMENT: Primary variables (Youden's index [YI > 0.8 vs. <0.8], abstract conclusion positivity [positive vs. neutral/negative], number of reported items on the Standards for Reporting of Diagnostic Accuracy Studies [STARD] for Abstract guideline) and confounding variables (prospective vs. retrospective/unreported, sample size, study duration, interobserver agreement assessment, subspecialty, modality) were extracted. STATISTICAL TESTS: Multivariable logistic regression to obtain adjusted odds ratio (OR) as a measure of the association between the primary variables and acceptance by radiology conferences and journals; 95% confidence intervals (CIs) and P-values were obtained; the threshold for statistical significance was P < 0.05. RESULTS: A total of 1000 conference abstracts (500 ESGAR and 500 ISMRM) and 1000 journal manuscripts (505 Radiology and 495 JMRI) were included. Conference abstract acceptance was not significantly associated with YI (adjusted OR = 0.97 for YI > 0.8; CI = 0.70-1.35), conclusion positivity (OR = 1.21 for positive conclusions; CI = 0.75-1.90) or STARD for Abstracts adherence (OR = 0.96 per unit increase in reported items; CI = 0.82-1.18). Manuscripts with positive abstract conclusions were less likely to be accepted by radiology journals (OR = 0.45; CI = 0.24-0.86), while YI (OR = 0.85; CI = 0.56-1.29) and STARD for Abstracts adherence (OR = 1.06; CI = 0.87-1.30) showed no significant association. Positive conclusions were present in 86.7% of submitted conference abstracts and 90.2% of journal manuscripts. DATA CONCLUSION: Diagnostic test accuracy studies with positive findings were not preferentially accepted by the evaluated radiology conferences or journals. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Safety and efficacy of flecainide associated with beta-blockers in arrhythmogenic right ventricular cardiomyopathy.

AIMS: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited cardiomyopathy associated with a high risk of ventricular arrhythmia (VA). Current guidelines recommend beta-blockers as first-line medical therapy and if ineffective, sotalol or amiodarone. We describe our experience, as a tertiary centre for ARVC, with the effectiveness and tolerance of flecainide in addition to beta-blockers to prevent VA in ARVC. METHODS AND RESULTS: We retrospectively included 100 consecutive ARVC patients who received flecainide with beta-blockers between May 1999 and November 2017. Treatment persistence and related side effects were assessed, as was VA-free survival on treatment, 24-h Holter monitoring and programmed ventricular stimulation (PVS) off- and on-treatment. Tolerance was good, with 10% flecainide discontinuations (lack of efficacy in six, atrial fibrillation in one, and side effects in three). No Brugada-induced electrocardiography pattern on flecainide or haemodynamic impairment was reported. Premature ventricular contraction burden at 24-h Holter monitoring was significantly decreased under treatment [median 415 (interquartile range, IQR 97-730) vs. 2370 (1572-3400) at baseline, P < 0.0001, n = 46]. Among the 33 patients with PVS under treatment, PVS was positive in 40% on-treatment vs. 94% off-treatment (P < 0.001). During a median follow-up of 47 months (IQR 23-73), 22 patients presented sustained VA on treatment, corresponding to an event rate of 5% [95% confidence interval (CI) (0.6-9)] at 1 year and 25% [95% CI (14-35)] at 5 years under treatment. No patient died. CONCLUSION: This study suggests that flecainide and beta-blockers association is complementary to implantable cardioverter-defibrillator and catheter ablation and is safe for treating persistent symptomatic VA in patients with ARVC.

Tweeting Bias in Diagnostic Test Accuracy Research: Does Title or Conclusion Positivity Influence Dissemination?

PURPOSE: To examine if tweeting bias exists within imaging literature by determining if diagnostic test accuracy (DTA) studies with positive titles or conclusions are tweeted more than non-positive studies. METHODS: DTA studies published between October 2011 to April 2016 were included. Positivity of titles and conclusions were assessed independently and in duplicate, with disagreements resolved by consensus. A negative binomial regression analysis controlling for confounding variables was performed to assess the relationship between title or conclusion positivity and tweets an article received in the 100 days post-publication. RESULTS: 354 DTA studies were included. Twenty-four (7%) titles and 300 (85%) conclusions were positive (or positive with qualifier); 1 (0.3%) title and 23 (7%) conclusions were negative; and 329 (93%) titles and 26 (7%) conclusions were neutral. Studies with positive, negative, and neutral titles received a mean of 0.38, 0.00, and 0.45 tweets per study; while those with positive, negative, and neutral conclusions received a mean of 0.44, 0.61, and 0.38 tweets per study. Regression coefficients were -0.05 (SE 0.46) for positive relative to non-positive titles, and -0.09 (SE 0.31) for positive relative to non-positive conclusions. The positivity of the title (P = 0.91) or conclusion (P = 0.76) was not significantly associated with the number of tweets an article received. CONCLUSIONS: The positivity of the title or conclusion for DTA studies does not influence the amount of tweets it receives suggesting that tweet bias is not present among imaging diagnostic accuracy studies. Study protocol available at https://osf.io/hdk2m/.

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction.

Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.

Documentation of Shared Decisionmaking in the Emergency Department.

STUDY OBJECTIVE: While patient-centered communication and shared decisionmaking are increasingly recognized as vital aspects of clinical practice, little is known about their characteristics in real-world emergency department (ED) settings. We constructed a natural language processing tool to identify patient-centered communication as documented in ED notes and to describe visit-level, site-level, and temporal patterns within a large health system. METHODS: This was a 2-part study involving (1) the development and validation of an natural language processing tool using regular expressions to identify shared decisionmaking and (2) a retrospective analysis using mixed effects logistic regression and trend analysis of shared decisionmaking and general patient discussion using the natural language processing tool to assess ED physician and advanced practice provider notes from 2013 to 2020. RESULTS: Compared to chart review of 600 ED notes, the accuracy rates of the natural language processing tool for identification of shared decisionmaking and general patient discussion were 96.7% (95% CI 94.9% to 97.9%) and 88.9% (95% confidence interval [CI] 86.1% to 91.3%), respectively. The natural language processing tool identified shared decisionmaking in 58,246 (2.2%) and general patient discussion in 590,933 (22%) notes. From 2013 to 2020, natural language processing-detected shared decisionmaking increased 300% and general patient discussion increased 50%. We observed higher odds of shared decisionmaking documentation among physicians versus advanced practice providers (odds ratio [OR] 1.14, 95% CI 1.07 to 1.23) and among female versus male patients (OR 1.13, 95% CI 1.11 to 1.15). Black patients had lower odds of shared decisionmaking (OR 0.8, 95% CI 0.84 to 0.88) compared with White patients. Shared decisionmaking and general patient discussion were also associated with higher levels of triage and commercial insurance status. CONCLUSION: In this study, we developed and validated an natural language processing tool using regular expressions to extract shared decisionmaking from ED notes and found multiple potential factors contributing to variation, including social, demographic, temporal, and presentation characteristics.

Identifying and Engineering Bottlenecks of Autotrophic Isobutanol Formation in Recombinant C. ljungdahlii by Systemic Analysis.

Clostridium ljungdahlii (C. ljungdahlii, CLJU) is natively endowed producing acetic acid, 2,3-butandiol, and ethanol consuming gas mixtures of CO2, CO, and H2 (syngas). Here, we present the syngas-based isobutanol formation using C. ljungdahlii harboring the recombinant amplification of the "Ehrlich" pathway that converts intracellular KIV to isobutanol. Autotrophic isobutanol production was studied analyzing two different strains in 3-L gassed and stirred bioreactors. Physiological characterization was thoroughly applied together with metabolic profiling and flux balance analysis. Thereof, KIV and pyruvate supply were identified as key "bottlenecking" precursors limiting preliminary isobutanol formation in CLJU[KAIA] to 0.02 g L-1. Additional blocking of valine synthesis in CLJU[KAIA]:ilvE increased isobutanol production by factor 6.5 finally reaching 0.13 g L-1. Future metabolic engineering should focus on debottlenecking NADPH availability, whereas NADH supply is already equilibrated in the current generation of strains.

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires.

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel deep learning method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.