Safety Management as a Foundation for Evidence-Based Aeromedical Standards and Reporting of Medical Events
Bibliographic record
Abstract
The different interpretations by States (countries) of the aeromedical standards established by the International Civil Aviation Organization has resulted in a variety of approaches to the development of national aeromedical policy, and consequently a relative lack of harmonization. However, in many areas of aviation, safety management systems have been recently introduced and may represent a way forward. A safety management system can be defined as "A systematic approach to managing safety, including the necessary organizational structures, accountabilities, policies, and procedures" (1). There are four main areas where, by applying safety management principles, it may be possible to better use aeromedical data to enhance flight safety. These are: 1) adjustment of the periodicity and content of routine medical examinations to more accurately reflect aeromedical risk; 2) improvement in reporting and analysis of routine medical examination data; 3) improvement in reporting and analysis of in-flight medical events; and 4) support for improved reporting of relevant aeromedical events through the promotion of an appropriate culture by companies and regulatory authorities. This paper explores how the principles of safety management may be applied to aeromedical systems to improve their contribution to safety.
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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 | 0.082 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".