Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years.

Third molar development is used for dental age estimation when all the other teeth are fully mature. In most medicolegal facilities, dental age estimation is an operator-dependent procedure. During the examination of unaccompanied and undocumented minors, this procedure may lead to binary decisions around age thresholds of legal interest, namely the ages of 14, 16 and 18 years. This study aimed to test the performance of artificial intelligence to classify individuals below and above the legal age thresholds of 14, 16 and 18 years using third molar development. The sample consisted of 11,640 panoramic radiographs (9680 used for training and 1960 used for validation) of males (n = 5400) and females (n = 6240) between 6 and 22.9 years. Computer-based image annotation was performed with V7 software (V7labs, London, UK). The region of interest was the mandibular left third molar (T38) outlined with a semi-automated contour. DenseNet121 was the Convolutional Neural Network (CNN) of choice and was used with Transfer Learning. After Receiver-operating characteristic curves, the area under the curve (AUC) was 0.87 and 0.86 to classify males and females below and above the age of 14, respectively. For the age threshold of 16, the AUC values were 0.88 (males) and 0.83 (females), while for the age of 18, AUC were 0.94 (males) and 0.83 (females). Specificity rates were always between 0.80 and 0.92. Artificial intelligence was able to classify male and females below and above the legal age thresholds of 14, 16 and 18 years with high accuracy.

When do the effects of single-session interventions persist? Testing the mindset + supportive context hypothesis in a longitudinal randomized trial.

Background: Single-session interventions have the potential to address young people's mental health needs at scale, but their effects are heterogeneous. We tested whether the mindset + supportive context hypothesis could help explain when intervention effects persist or fade over time. The hypothesis posits that interventions are more effective in environments that support the intervention message. We tested this hypothesis using the synergistic mindsets intervention, a preventative treatment for stress-related mental health symptoms that helps students appraise stress as a potential asset in the classroom (e.g., increasing oxygenated blood flow) rather than debilitating. In an introductory college course, we examined whether intervention-consistent messages from instructors sustained changes in appraisals over time, as well as impacts on students' predisposition to try demanding academic tasks that could enhance learning. Methods: We randomly assigned 1675 students in the course to receive the synergistic mindsets intervention (or a control activity) at the beginning of the semester, and subsequently, to receive intervention-supportive messages from their instructor (or neutral messages) four times throughout the term. We collected weekly measures of students' appraisals of stress in the course and their predisposition to take on academic challenges. Trial-registration: OSF.io; DOI: 10.17605/osf.io/fchyn. Results: A conservative Bayesian analysis indicated that receiving both the intervention and supportive messages led to the greatest increases in positive stress appraisals (0.35 SD; 1.00 posterior probability) and challenge-seeking predisposition (2.33 percentage points; 0.94 posterior probability), averaged over the course of the semester. In addition, intervention effects grew larger throughout the semester when complemented by supportive instructor messages, whereas without these messages, intervention effects shrank somewhat over time. Conclusions: This study shows, for the first time, that supportive cues in local contexts can be the difference in whether a single-session intervention's effects fade over time or persist and even amplify.

Why Meta-Analyses of Growth Mindset and Other Interventions Should Follow Best Practices for Examining Heterogeneity: Commentary on Macnamara and Burgoyne (2023) and Burnette et al. (2023).

Meta-analysts often ask a yes-or-no question: Is there an intervention effect or not? This traditional, all-or-nothing thinking stands in contrast with current best practice in meta-analysis, which calls for a heterogeneity-attuned approach (i.e., focused on the extent to which effects vary across procedures, participant groups, or contexts). This heterogeneity-attuned approach allows researchers to understand where effects are weaker or stronger and reveals mechanisms. The current article builds on a rare opportunity to compare two recent meta-analyses that examined the same literature (growth mindset interventions) but used different methods and reached different conclusions. One meta-analysis used a traditional approach (Macnamara and Burgoyne, in press), which aggregated effect sizes for each study before combining them and examined moderators one-by-one by splitting the data into small subgroups. The second meta-analysis (Burnette et al., in press) modeled the variation of effects within studies-across subgroups and outcomes-and applied modern, multi-level meta-regression methods. The former concluded that growth mindset effects are biased, but the latter yielded nuanced conclusions consistent with theoretical predictions. We explain why the practices followed by the latter meta-analysis were more in line with best practices for analyzing large and heterogeneous literatures. Further, an exploratory re-analysis of the data showed that applying the modern, heterogeneity-attuned methods from Burnette et al. (in press) to the dataset employed by Macnamara and Burgoyne (in press) confirmed Burnette et al.'s conclusions; namely, that there was a meaningful, significant effect of growth mindset in focal (at-risk) groups. This article concludes that heterogeneity-attuned meta-analysis is important both for advancing theory and for avoiding the boom-or-bust cycle that plagues too much of psychological science.

Electronic cigarette use by and perceptions of middle and high school students in the United States.

Electronic cigarettes (e-cigarettes) provide a novel source of nicotine and may lead to experimentation by adolescents and eventually to their use of conventional cigarettes. The National Youth Tobacco Survey collected information from a nationally representative sample of students in 2016 to determine their experiences with conventional cigarettes and e-cigarettes, their intentions to use these products in the future, and their perceptions of harm and addiction associated with these products. We analyzed these data with ordered probit regression models to determine possible associations with the intention to try e-cigarettes and conventional cigarettes. This survey included 20,520 respondents, 50.6% were male, 5.9% had used conventional cigarettes, and 9.0% had used e-cigarettes. Approximately 10% thought e-cigarettes caused no harm, 3.7% thought that conventional cigarettes caused no harm, and 52.5% thought that e-cigarettes were less harmful than conventional cigarettes. Less than 1% of the students responded "Definitely yes" to the question, "Do you think you will try an e-cig?" The odds ratios for an intention to try e-cigarettes increased as the perception of harm decreased; these ratios increased from 1.0 for "A lot of harm" to 5.85 (95% CI: 3.51, 9.75) for "No harm." In 2016, the majority of students thought that e-cigarettes could cause some harm. This survey indicates that most students have not tried e-cigarettes or conventional cigarettes. The minority of students who think that e-cigarettes pose no harm and students in the ages 14 and 16 are more likely to try them.

The Creation of LIFE-M: The Longitudinal, Intergenerational Family Electronic Micro-Database Project.

This paper describes the creation of the Longitudinal, Intergenerational Family Electronic Micro-Database (LIFE-M), a new data resource linking vital records and decennial censuses for millions of individuals and families living in the late 19th and 20th centuries in the United States. This combination of records provides a life-course and intergenerational perspective on the evolution of health and economic outcomes. Vital records also enable the linkage of women, because they contain a crosswalk between women's birth (i.e., "maiden") and married names. We describe (1) the data sources, coverage, and linking sequence; (2) the process and supervised machine-learning methods to linking records longitudinally and across generations; and (3) the resulting linked samples, including linking rates, representativeness, and weights.

Diagnostic performance of convolutional neural networks for dental sexual dimorphism.

Convolutional neural networks (CNN) led to important solutions in the field of Computer Vision. More recently, forensic sciences benefited from the resources of artificial intelligence, especially in procedures that normally require operator-dependent steps. Forensic tools for sexual dimorphism based on morphological dental traits are available but have limited performance. This study aimed to test the application of a machine learning setup to distinguish females and males using dentomaxillofacial features from a radiographic dataset. The sample consisted of panoramic radiographs (n = 4003) of individuals in the age interval of 6 and 22.9 years. Image annotation was performed with V7 software (V7labs, London, UK). From Scratch (FS) and Transfer Learning (TL) CNN architectures were compared, and diagnostic accuracy tests were used. TL (82%) performed better than FS (71%). The correct classifications of females and males aged ≥ 15 years were 87% and 84%, respectively. For females and males < 15 years, the correct classifications were 80% and 83%, respectively. The Area Under the Curve (AUC) from Receiver-operating Characteristic (ROC) curves showed high classification accuracy between 0.87 and 0.91. The radio-diagnostic use of CNN for sexual dimorphism showed positive outcomes and promising forensic applications to the field of dental human identification.

A synergistic mindsets intervention protects adolescents from stress.

Social-evaluative stressors-experiences in which people feel they could be judged negatively-pose a major threat to adolescent mental health1-3 and can cause young people to disengage from stressful pursuits, resulting in missed opportunities to acquire valuable skills. Here we show that replicable benefits for the stress responses of adolescents can be achieved with a short (around 30-min), scalable 'synergistic mindsets' intervention. This intervention, which is a self-administered online training module, synergistically targets both growth mindsets4 (the idea that intelligence can be developed) and stress-can-be-enhancing mindsets5 (the idea that one's physiological stress response can fuel optimal performance). In six double-blind, randomized, controlled experiments that were conducted with secondary and post-secondary students in the United States, the synergistic mindsets intervention improved stress-related cognitions (study 1, n = 2,717; study 2, n = 755), cardiovascular reactivity (study 3, n = 160; study 4, n = 200), daily cortisol levels (study 5, n = 118 students, n = 1,213 observations), psychological well-being (studies 4 and 5), academic success (study 5) and anxiety symptoms during the 2020 COVID-19 lockdowns (study 6, n = 341). Heterogeneity analyses (studies 3, 5 and 6) and a four-cell experiment (study 4) showed that the benefits of the intervention depended on addressing both mindsets-growth and stress-synergistically. Confidence in these conclusions comes from a conservative, Bayesian machine-learning statistical method for detecting heterogeneous effects6. Thus, our research has identified a treatment for adolescent stress that could, in principle, be scaled nationally at low cost.

Teacher Mindsets Help Explain Where a Growth-Mindset Intervention Does and Doesn't Work.

A growth-mindset intervention teaches the belief that intellectual abilities can be developed. Where does the intervention work best? Prior research examined school-level moderators using data from the National Study of Learning Mindsets (NSLM), which delivered a short growth-mindset intervention during the first year of high school. In the present research, we used data from the NSLM to examine moderation by teachers' mindsets and answer a new question: Can students independently implement their growth mindsets in virtually any classroom culture, or must students' growth mindsets be supported by their teacher's own growth mindsets (i.e., the mindset-plus-supportive-context hypothesis)? The present analysis (9,167 student records matched with 223 math teachers) supported the latter hypothesis. This result stood up to potentially confounding teacher factors and to a conservative Bayesian analysis. Thus, sustaining growth-mindset effects may require contextual supports that allow the proffered beliefs to take root and flourish.

A Synergistic Mindsets Intervention Protects Adolescents from Social Stress.

Social stress poses a major threat to adolescent health via its effects on internalizing symptoms, such as anxiety and depression. Available interventions to help adolescents improve their stress responses, however, have not been effective in rigorous evaluation studies, or they have been difficult to administer widely. Here we show that replicable improvements in adolescent stress responses can be achieved with a short (~30-minute), scalable synergistic mindsets intervention. This intervention, which is a self-administered online training module, targets both growth mindsets (the idea that people's intelligence can be developed in response to challenge) and stress-can-be-enhancing mindsets (the idea that people's stress responses can be fuel for optimal performance). Its goal is to promote positive engagement with stressful events (e.g., learning from failure on a quiz or a conflict with a peer) and to encourage adolescents to use their responses to stressful events and even their bodily symptoms (e.g. racing heart, sweaty palms, butterflies in their stomach) to their advantage. In five double-blind, randomized, controlled trials (total N = 4,091 adolescents), the new synergistic mindsets intervention improved stress-related cognitions (Studies 1-2), cardiovascular reactivity (Study 3), daily internalizing symptoms and cortisol levels (Study 4), and generalized anxiety symptoms during the 2020 COVID-19 lockdowns (Study 5). Effects on downstream outcomes (in Studies 3-5) were stronger among individuals who, at baseline, held the two negative mindsets targeted by the intervention, providing evidence for the proposed mechanisms. Confidence in this conclusion comes from a conservative, Bayesian machine-learning method for detecting heterogeneity.

Thrombocytopenia and clear corneal incision cataract surgery.

PURPOSE: To investigate complications and outcomes of clear corneal incision cataract surgery in patients with thrombocytopenia. SETTING: 1 veterans hospital and 2 academic medical centers. DESIGN: Multicenter retrospective chart review. METHODS: All eyes of thrombocytopenic patients that underwent clear corneal incision cataract surgery with a platelet count of 100 × 103/µL or less measured within 30 days prior to surgery were included. Subject demographics, intraoperative complications, use of pupillary expansion devices, use of local anesthetic injections, and change in corrected distance visual acuity were recorded. RESULTS: 3 sites recorded 40 113 clear corneal incision cataract surgeries, of which 196 eyes (0.49%) of 150 thrombocytopenic patients were recorded. The mean platelet count in the study subjects was 73.0 ± 20.5 × 103/µL. Two cases of intraoperative iris hemorrhage that were readily and controlled occurred in conjunction with pupillary expansion. There were no bleeding complications associated with retrobulbar, peribulbar, or sub-Tenon anesthetic injections. There was a statistically significant improvement (P < .0001) in visual acuity post-operatively. CONCLUSIONS: Clear corneal incision cataract surgery with pupillary expansion devices and local anesthetic injections can be safely performed in patients with thrombocytopenia.

Clinical Characteristics of a Case Series of Ocular Syphilis With 3 Cases Requiring Multiple Vitrectomies After Late Diagnosis.

Purpose: This work describes the characteristics and unique features of ocular syphilis. Methods: Ten serologically proven cases of ocular syphilis were retrospectively analyzed. Results: Eighteen eyes of 10 patients were affected. Nine of 10 patients were male and the mean age was 58 years (range, 36-81 years). HIV antibody testing was positive in 3 patients (30%). Five cases were first diagnosed by an ophthalmologist. One patient presented with a syphilitic rash. The most common ocular findings were panuveitis (n = 6) and cystoid macular edema (n = 4). Ocular involvement was unilateral in 2 cases and bilateral in 8. Best-corrected visual acuity improved in 13 of 18 eyes (72%) after treatment. Three cases developed recurrent retinal detachments that required repair with silicone oil. Conclusions: Most cases were HIV negative. Syphilitic uveitis can be the initial presentation of syphilis without classic systemic manifestation. Ophthalmologists play an important role in the diagnosis and treatment of syphilis.

A national experiment reveals where a growth mindset improves achievement.

A global priority for the behavioural sciences is to develop cost-effective, scalable interventions that could improve the academic outcomes of adolescents at a population level, but no such interventions have so far been evaluated in a population-generalizable sample. Here we show that a short (less than one hour), online growth mindset intervention-which teaches that intellectual abilities can be developed-improved grades among lower-achieving students and increased overall enrolment to advanced mathematics courses in a nationally representative sample of students in secondary education in the United States. Notably, the study identified school contexts that sustained the effects of the growth mindset intervention: the intervention changed grades when peer norms aligned with the messages of the intervention. Confidence in the conclusions of this study comes from independent data collection and processing, pre-registration of analyses, and corroboration of results by a blinded Bayesian analysis.

Cervical Spine Alignment in Helmeted Skiers and Snowboarders With Suspected Head and Neck Injuries: Comparison of Lateral C-spine Radiographs Before and After Helmet Removal and Implications for Ski Patrol Transport.

OBJECTIVE: Current protocols for spine immobilization of the injured skier/snowboarder have not been scientifically validated. Observing changes in spine alignment during common rescue scenarios will help strengthen recommendations for rescue guidelines. METHODS: Twenty-eight healthy volunteers (18 men, 10 women) age 47±17 (range 20-73) (mean ±SD with range) underwent a mock rescue in which candidate patrollers completing an Outdoor Emergency Care course performed spine immobilization and back boarding in 3 scenarios: 1) Ski helmet on, no c-collar; 2) helmet on, with c-collar; and 3) helmet removed, with c-collar. After each scenario, a lateral radiograph was taken of the cervical spine to observe for changes in alignment. RESULTS: Compared with the control group (helmet on, no collar), we observed 9 degrees of increased overall (occiput-C7) cervical extension in the helmet on, with collar group (P < .001), and 17 degrees in the helmet off, with collar group (P < .001). There was increased extension at the occiput-C2 intersegment in the helmet on, with collar group (9 degrees, P < .001) and at both the occiput-C2 (9 degrees, P < .001) and C2-C7 (8 degrees, P < .001) intersegments in the helmet off, with collar group. CONCLUSION: Ski helmet removal and c-collar application each leads to increased extension of the cervical spine. In the absence of other clinical factors, our recommendation is that helmets should be left in place and c-collars not routinely applied during ski patrol rescue.

Neuromuscular Training Availability and Efficacy in Preventing Anterior Cruciate Ligament Injury in High School Sports: A Retrospective Cohort Study.

OBJECTIVE: To document neuromuscular training (NMT) availability and its relationship to anterior cruciate ligament (ACL) injuries in 4 major high school sports by gender, sport, and rural/urban geography, with the hypothesis that increased exposure to NMT would be associated with fewer ACL injuries. DESIGN: A retrospective cohort study. SETTING: All Minnesota high schools identified in the Minnesota State High School League (MSHSL) database for fall 2014 boys' football and soccer, and girls' volleyball and soccer. PARTICIPANTS: All high school athletic directors were surveyed to report their school's fall 2014 experience; 53.5% returned the survey reporting experience with one or more of the sports. INTERVENTION: Athletic directors documented each sport's preseason and in-season exposure to NMT (plyometric exercises, proximal/core muscle strengthening, education and feedback regarding proper body mechanics, and aerobics) and licensed athletic trainers. MAIN OUTCOMES: Reported ACL injuries by sport, gender and rural/urban. RESULTS: More than two-thirds of teams incorporated facets of NMT into their sport. Among male athletes, soccer players exposed to licensed athletic trainers experienced significantly fewer ACL injuries (P < 0.005), and NMT was associated with significantly fewer ACL injuries in football (P < 0.05) and soccer (P < 0.05). Female athletes did not demonstrate similar associated improvements, with volleyball injuries associated with increased NMT (P < 0.001), and soccer injuries not associated with NMT. However, girl soccer players in rural settings reported fewer ACL injures compared with urban teams (P < 0.001). CONCLUSIONS: Most fall high school sports teams were exposed to NMT, which was associated with fewer ACL injuries for male, but not for female athletes. Improved gender- and sport-specific preventive training programs are indicated.

Leber Hereditary Optic Neuropathy: Visual Recovery in a Patient With the Rare m.3890G>A Point Mutation.

A 15-year-old boy experienced painless vision loss in the left eye of unknown duration. Leber hereditary optic neuropathy (LHON) was suspected, despite negative testing for the 3 most common pathogenic gene mutations and idebenone 300 mg 3 times daily was prescribed. Nine months later, the patient developed right eye involvement. Complete mitochondrial genome analysis revealed 2 rare variants-m.3890G>A of the MT-ND1 gene and m.8417C>A of the MT-ATP8 gene. The former has been described in severe infantile Leigh syndrome and LHON; the latter is of unknown significance. The patient experienced progressive visual deterioration through 12 months, but improved to 20/20, right eye and 20/25, left eye, at 21 months. Visual recovery can occur in a patient with bilateral optic neuropathy secondary to the rare m.3890G>A point mutation.

Bayesian Gaussian Copula Factor Models for Mixed Data.

Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent Gaussian variables or through generalized latent trait models acommodating measurements in the exponential family. However, when generalizing to non-Gaussian measured variables the latent variables typically influence both the dependence structure and the form of the marginal distributions, complicating interpretation and introducing artifacts. To address this problem we propose a novel class of Bayesian Gaussian copula factor models which decouple the latent factors from the marginal distributions. A semiparametric specification for the marginals based on the extended rank likelihood yields straightforward implementation and substantial computational gains. We provide new theoretical and empirical justifications for using this likelihood in Bayesian inference. We propose new default priors for the factor loadings and develop efficient parameter-expanded Gibbs sampling for posterior computation. The methods are evaluated through simulations and applied to a dataset in political science. The models in this paper are implemented in the R package bfa.

Patient clustering with uncoded text in electronic medical records.

We propose a mixture model for text data designed to capture underlying structure in the history of present illness section of electronic medical records data. Additionally, we propose a method to induce bias that leads to more homogeneous sets of diagnoses for patients in each cluster. We apply our model to a collection of electronic records from an emergency department and compare our results to three other relevant models in order to assess performance. Results using standard metrics demonstrate that patient clusters from our model are more homogeneous when compared to others, and qualitative analyses suggest that our approach leads to interpretable patient sub-populations when applied to real data. Finally, we demonstrate an example of our patient clustering model to identify adverse drug events.