Tag: pubmed

Psychiatry. 2025 Spring;88(1):1-12. doi: 10.1080/00332747.2025.2460954. Epub 2025 Mar 24.

ABSTRACT

OBJECTIVE: Identifying the individual, environmental, and social characteristics of suicide events is central to developing a comprehensive public health strategy for intervention and prevention of suicide. The Haddon Matrix has previously been used as a tool to address this goal in various forms of injury prevention. The purpose of this paper is to apply the Haddon Matrix to suicide to inform research, prevention, and intervention.

METHODS: To examine the Haddon Matrix’s utility for suicide, a selective literature review was performed of recent and influential papers on risk of suicide across time, identifying contributing factors, conditions, policies, theories, and prevention strategies. Second, the key factors identified were organized on the Haddon Matrix. Third, to examine the pragmatic utility of the Haddon Matrix, the Department of Defense (DoD) Suicide Prevention and Response Independent Review Committee’s (SPRIRC) recommendations to prevent military suicides were placed on the Haddon Matrix, and the areas covered were examined.

RESULTS: Characteristics of suicide across pre-event, event, and post-event stages were identified at the individual (host), agent (factors related to the method used for suicide), environmental, and social domains. These were used to construct a Suicide Haddon Matrix. Placing the DoD SPRIRC recommendations on the Haddon Matrix showed a concentration of high priority recommendations in the pre-event, host, and agent domains.

CONCLUSION: A Suicide Haddon Matrix can guide researchers, mental health practitioners, and public health officials to key host, agent, physical, and social environment factors. This can identify areas that need research and opportunities for intervention and prevention.

PMID:40127289 | DOI:10.1080/00332747.2025.2460954

Proc Natl Acad Sci U S A. 2025 Apr;122(13):e2418616122. doi: 10.1073/pnas.2418616122. Epub 2025 Mar 24.

ABSTRACT

In response to the alarming recent decline in US math achievement, we conducted a national megastudy in which 140,461 elementary school teachers who collectively taught 2,992,027 students were randomly assigned to receive a variety of behaviorally informed email nudges aimed at improving students’ progress in math. Specifically, we partnered with the nonprofit educational platform Zearn Math to compare the impact of 15 different interventions with a reminder-only megastudy control condition. All 16 conditions entailed weekly emails delivered to teachers over 4-wk in the fall of 2021. The best-performing intervention, which encouraged teachers to log into Zearn Math for an updated report on how their students were doing that week, produced a 5.06% increase in students’ math progress (3.30% after accounting for the winner’s curse). In exploratory analyses, teachers who received any behaviorally informed email nudge (vs. a reminder-only megastudy control) saw their students’ math progress boosted by an average of 1.89% during the 4-wk intervention period; emails referencing personalized data (i.e., classroom-specific statistics) outperformed emails that did not by 2.26%. While small in size, these intervention effects were consistent across school socioeconomic status and school type (public, private, etc.) and, further, persisted in the 8-wk post-intervention period. Collectively, these findings underscore both how difficult it is to change behavior and the need for large-scale, rigorous, empirical research of the sort undertaken in this megastudy.

PMID:40127270 | DOI:10.1073/pnas.2418616122

Appl Immunohistochem Mol Morphol. 2025 Mar 25. doi: 10.1097/PAI.0000000000001257. Online ahead of print.

ABSTRACT

We aimed to investigate molecular mechanisms affecting melanoma progression by comparing genetic/epigenetic features between melanomas of different Breslow thickness and stage using TCGA (The Cancer Genome Atlas) data. The TCGA, Firehose Legacy, melanoma data set was utilized on the cBioPortal website. The cases were compared in terms of mRNA expression and DNA methylation. Gene Ontology (GO) and KEGG pathways enrichment analysis were performed using the online WebGestalt tool. STRING and Cytoscape software were used to construct a protein-protein interaction network and identify hub genes. P and q<0.05, FDR< 0.05 were considered statistically significant. 1001 differentially expressed genes were identified between thin (≤1 mm) and thick (>1 mm) melanomas. Pathway analyses revealed that genes enriched in thin melanomas were associated with adaptive immune response, T-cell activation, immune response regulation, leukocyte, and cytokine-related pathways, whereas genes enriched in thick melanomas were related to epidermis development. Ten hub genes were identified (CD4, IFNG, PTPRC, CD8A, CTLA4, CD69, ICOS, CD27, CD28, CD19). All of these genes are involved in crucial immunological processes. Understanding the complex changes in melanoma progression is essential for accurate diagnosis and prediction of prognosis. Our results may shed light on subsequent studies to identify the steps in melanoma progression.

PMID:40127249 | DOI:10.1097/PAI.0000000000001257

J Craniofac Surg. 2025 Mar 24. doi: 10.1097/SCS.0000000000011268. Online ahead of print.

ABSTRACT

OBJECTIVE: Explore a clinical-led approach that applies digital 3-dimensional (3D) reconstruction technology to titanium mesh design and production to achieve more cost-effective and personalized skull repair goals.

MATERIALS AND METHODS: To develop a clinician-led titanium mesh fabrication method, which is achieved by digitally 3D reconstructing the patient’s own medical imaging data and designing a personalized mold for cranial defects, physically constructing the mold using 3D printing technology, and ultimately shaping the titanium mesh according to the patient’s personalized mold by an experienced clinician. A total of 60 cases of adult patients with cranial defects who met the inclusion criteria of this study and were seen in the Department of Neurosurgery from January 2022 to January 2024 were collected for retrospective analysis.

RESULTS: A total of 30 cases of cranial defect molds and personalized repair titanium mesh were made by 3D printing technology. All the molds and personalized repair titanium mesh were successfully produced, and all the titanium mesh matched with the molds, and the time consumed for the production of titanium mesh in group A was less than that in group B, with a statistically significant difference (P<0.001), and the cost of production of titanium mesh in group A was more economically advantageous than that in group B (P=0.002).

CONCLUSION: The approach of titanium mesh designed and fabricated autonomously by clinicians in conjunction with 3D printing technology is less time-consuming, more economical, and highly personalized.

PMID:40127223 | DOI:10.1097/SCS.0000000000011268

J Am Acad Orthop Surg. 2025 Mar 25. doi: 10.5435/JAAOS-D-24-01232. Online ahead of print.

ABSTRACT

INTRODUCTION: Surgery and postoperative opioid prescriptions are critical periods for potential drug dependence and diversion. Enhanced recovery after surgery (ERAS) pathways aim to improve patient outcomes by leveraging preoperative education, emphasizing nonopioid pain management, and using less invasive surgical techniques. The study hypothesis was that the use of ERAS pathways would decrease postoperative opioid prescribing after arthroscopy and arthroplasty surgeries.

METHODS: A retrospective chart review was conducted on patients treated by 11 orthopaedic surgeons at 9 Iowa hospitals from November 2022 to March 2024. Patients were divided into arthroplasty (n = 67) and arthroscopy (n = 33) cohorts. Opioids prescribed before and after ERAS implementation were measured and converted to morphine milligram equivalents (MMEs). Statistical analyses included the Wilcoxon signed rank test, Mann-Whitney U test, and chi-squared test.

RESULTS: The mean pre-ERAS prescription size was 389 MMEs (range: 140 to 900 MMEs) for the overall cohort postoperatively, with arthroplasty at 451 MMEs (range: 200 to 900 MMEs) and arthroscopy at 264 MMEs (range: 140 to 450 MMEs). After ERAS, the overall mean size dropped to 194 MMEs (range: 38 to 600 MMEs), with arthroplasty at 210 MMEs (range: 38 to 600 MMEs) and arthroscopy at 161 MMEs (range: 45 to 315 MMEs). Both cohorts saw significant reductions, with a mean 47% reduction in arthroplasty and a mean 33% reduction in arthroscopy (both P < 0.001). Statistical analysis found percent reduction of prescription size to be greater in the arthroplasty cohort than in the arthroscopy cohort (P < 0.001). Arthroscopy patients had a higher mean percentage of MMEs prescribed leftover (60%) compared with arthroplasty patients (27%; P< 0.001).

CONCLUSION: The study hypothesis was upheld as ERAS pathways resulted in a notable reduction in prescribing of opioids postoperatively after both arthroplasty and arthroscopic surgeries. ERAS pathways should continue to be tailored and studied to improve postoperative recovery while decreasing the reliance on opioids postoperatively for pain management.

PMID:40127220 | DOI:10.5435/JAAOS-D-24-01232

J Craniofac Surg. 2025 Mar 24. doi: 10.1097/SCS.0000000000011258. Online ahead of print.

ABSTRACT

INTRODUCTION: Accurate and efficient medical coding is essential for proper reimbursement and health care management. Current Procedural Terminology (CPT) codes, derived from operative notes, standardize medical billing but are often prone to variability and errors due to the complexity of surgical procedures. With advancements in artificial intelligence (AI), tools like ChatGPT and other large language models (LLMs) are being explored for their potential to automate coding tasks. This study evaluates the ability of LLMs to generate accurate CPT codes for craniofacial surgical procedures based on operative notes.

METHODS: Operative notes for 10 craniofacial surgical cases were collected from a single surgeon at Nemours Children’s Health. The notes were provided to AI tools (ChatGPT 4.0 and Gemini) to generate corresponding CPT codes. These AI-generated codes were compared against manually coded results by expert reviewers. Responses were evaluated for accuracy against manually generated CPT codes and classified as correct, partially correct, or incorrect.

RESULTS: ChatGPT and Gemini demonstrated similar performance in generating CPT codes, with no statistically significant differences in accuracy or correctness between the models (P > 0.999). Gemini produced a slightly higher proportion of correct responses (30% versus 20%), whereas ChatGPT had more partially correct responses (50% versus 40%).

CONCLUSIONS: This study demonstrates that AI may be a clinically valuable resource for craniofacial CPT coding, reducing administrative burden and increasing coding accuracy. Findings from this research could inform the integration of AI into medical billing practices, promoting efficiency in surgical specialties. Future research will explore generalizability to other surgical domains and refinement of AI models for coding tasks.

PMID:40127215 | DOI:10.1097/SCS.0000000000011258

J Am Acad Orthop Surg. 2025 Mar 25. doi: 10.5435/JAAOS-D-24-00372. Online ahead of print.

ABSTRACT

INTRODUCTION: External fixation costs have been identified as a primary driver of initial cost in the care of tibial plateau fractures. Because hospital systems and institutions pursue value-based care, external fixation choices become a uniquely surgeon-dependent driver of cost. Our objective was to determine differences in cost in a prepackaged, single-use, external fixation system compared with standard, modular, knee spanning frames. Secondary objectives were to determine differences in surgical time and loss of distraction between the two types of fixation.

METHODS: This was a retrospective cohort study at an academic level 1 trauma center. Fifty-nine patients were treated with knee spanning external fixation over a 7-year period (Arbeitsgemeinschaft fur Osteosynthesefragen/Orthopedic Trauma Association 41-B/C). Patients received either the Zimmer FastFrame external fixator or a conventional-style, modular, external fixator. The primary outcome was implant and supply cost. The secondary outcomes were operating room facility cost, surgical time, and percent of distraction lost.

RESULTS: The FastFrame cohort demonstrated a 24.9% decrease in surgical times (29.2 vs. 38.9 minutes, P = 0.002), with a 37% decrease in supply and implant cost of conventional cohort (0.63x vs. 1x, P < 0.001). Operating room facility cost was less than the conventional cohort (0.72x vs. 1x, P = 0.41), and total cost was 21.8% less (0.78x vs. 1x, P = 0.07), although these did not reach statistical significance. The Fastframe cohort lost less distraction (72.6% vs. 62.8%, P = 0.02).

CONCLUSION: The FastFrame demonstrates a lower supply and implant cost, faster surgical times, and demonstrated clinical equivalence in regard to loss of distraction when compared with conventional, modular, external fixator.

LEVEL OF EVIDENCE: Diagnostic-Level III.

PMID:40127204 | DOI:10.5435/JAAOS-D-24-00372

Eur J Cardiothorac Surg. 2025 Mar 24:ezaf108. doi: 10.1093/ejcts/ezaf108. Online ahead of print.

ABSTRACT

OBJECTIVES: Bilateral endoscopic thoracic sympathectomy is an effective treatment for primary hyperhidrosis, yet the causes of its main side effect, compensatory sweating, remain unclear. This study aimed to identify risk factors for compensatory sweating in a long-term follow-up cohort.

METHODS: Patients who underwent bilateral endoscopic thoracic sympathectomy for primary hyperhidrosis between 2010 and 2023 (n = 98) were interviewed, and preoperative data were collected. Compensatory sweating was classified as mild, moderate, or severe according to the Society of Thoracic Surgeons guidelines. Quality of life was assessed using the Hyperhidrosis Disease Severity Scale from the International Hyperhidrosis Society. Logistic and ordinal Bayesian regression models were applied to analyze associations between predictors, compensatory sweating outcomes, and quality of life.

RESULTS: The procedure achieved an overall effectiveness of 94.38%, with 34.69% of patients reporting compensatory sweating, predominantly mild (26.53%). Nearly all patients (97.95%) experienced a ≥ 50% reduction in sweating, and 94.89% achieved ≥80% reduction. Higher hemoglobin levels and marijuana protected against compensatory sweating incidence and severity. Conversely, smoking and hyperhidrosis involving both hands and axillae increased compensatory sweating risk. Better quality of life outcomes were correlated with higher hemoglobin levels and female sex, while worse outcomes were associated with older age, higher BMI, and axillary involvement.

CONCLUSIONS: Long-term follow-up highlights key predictors for compensatory sweating, emphasizing the importance of tailored preoperative counseling. Identifying at-risk patients, such as smokers and those with low hemoglobin levels, is essential for improving outcomes and managing expectations in the treatment of primary hyperhidrosis.

PMID:40127189 | DOI:10.1093/ejcts/ezaf108

Clin Infect Dis. 2025 Mar 24:ciaf152. doi: 10.1093/cid/ciaf152. Online ahead of print.

ABSTRACT

BACKGROUND: In 2010, the World Health Organization (WHO) increased recommended doses of first-line tuberculosis (TB) drugs for children. This systematic review aimed to determine the proportion of children who develop adverse events (AEs) on first-line TB treatment and to determine whether there has been a change in toxicity observed with WHO 2010 dosing.

METHODS: We searched MEDLINE, Embase, Scopus, Cochrane Database of Systematic Reviews, WHO Global Index Medicus, and ClinicalTrials.gov for studies that reported AEs for children and adolescents ≤19 years old with TB disease receiving first-line medications. A meta-analysis of proportions was performed to generate pooled proportions of AEs. The protocol was registered with the International Prospective Register of Systematic Reviews (CRD42023418496).

RESULTS: Forty studies comprising 5,021 participants were included. 682 (13.6%) participants experienced 712 AEs; 60 (1.2%) participants experienced a change in therapy due to an AE. The proportion of children with any AE was significantly higher with WHO 2010 dosing (26%, 95% CI 18-34%) compared to pre-WHO 2010 dosing (8%, 95% CI 4-15%), as was the proportion of children who developed severe AEs. There was no statistically significant difference in hepatotoxicity before and after 2010 dosing recommendations, however, significant increases in hepatotoxicity were seen in several subgroups with 2010 dosing. There was substantial heterogeneity between studies; none were at high risk of bias.

CONCLUSIONS: Higher dose regimens in children were associated with increased AEs, raising caution for further dose increases and necessitating additional study of treatment tolerability. These findings are limited by publication bias in observational trials.

PMID:40127187 | DOI:10.1093/cid/ciaf152

Brief Bioinform. 2025 Mar 4;26(2):bbaf119. doi: 10.1093/bib/bbaf119.

ABSTRACT

Accurately predicting the pathogenicity of missense variants is crucial for improving disease diagnosis and advancing clinical research. However, existing computational methods primarily focus on general pathogenicity predictions, overlooking assessments of disease-specific conditions. In this study, we propose DS-MVP, a method capable of predicting disease-specific pathogenicity of missense variants in human genomes. DS-MVP first leverages a deep learning model pre-trained on a large general pathogenicity dataset to learn rich representation of missense variants. It then fine-tunes these representations with an XGBoost model on smaller datasets for specific diseases. We evaluated the learned representation by testing it on multiple binary pathogenicity datasets and gene-level statistics, demonstrating that DS-MVP outperforms existing state-of-the-art methods, such as MetaRNN and AlphaMissense. Additionally, DS-MVP excels in multi-label and multi-class classification, effectively classifying disease-specific pathogenic missense variants based on disease conditions. It further enhances predictions by fine-tuning the pre-trained model on disease-specific datasets. Finally, we analyzed the contributions of the pre-trained model and various feature types, with gene description corpus features from large language model and genetic feature fusion contributing the most. These results underscore that DS-MVP represents a broader perspective on pathogenicity prediction and holds potential as an effective tool for disease diagnosis.

PMID:40127180 | DOI:10.1093/bib/bbaf119