Health Exposure Records and Occupations (HERO) Summary: Development of Occupational Exposure Summary for clinical utility in Military Populations
Bibliographic record
Abstract
Abstract Introduction Military exposure summarization is critical for Veterans with complex environmental, occupational, or toxic exposures. Existing methods are limited by technical language, incompatible data formats, and difficulty in prioritizing information. Clinicians require concise, standardized, and easily interpretable exposure summaries to facilitate rapid assessment. This report is part of a broader programmatic effort to collate military exposure information from established, as well as new sources of data to improve VA exposure-informed healthcare. Methods In a collaborative effort, VA clinicians from multiple specialties participated in a structured clinical needs assessment interview to identify the most clinically useful information to be included in the Health Exposure Records and Occupations (HERO) summary. The interviews covered summary length, exposure prioritization, demographics, military occupational history, features characterizing exposure, resilience factors, health outcomes, and impact on clinical practice. Results Consensus recommendations prescribed a concise summary with clear language, basic military demographics, and critical military exposures that prioritize exposures that require further investigation. Based on recommendations, the HERO summary also includes types of exposure, proximity, route, symptoms at the time of exposure, exposure period, duration, frequency, and protective controls used. Conclusion This perspective piece not only assesses the clinical need for exposure summarization and the optimal format for the HERO summary, but also highlights its potential impact. The HERO summary, as a tool, offers improved time efficiency, consistency in exposure-informed care across the VA, enhances communication between Veterans and providers, and improves understanding of the association between military exposures and health outcomes, potentially transforming the VA healthcare system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".