Assessing aeromedical risk: a three-dimensional risk matrix approach
Bibliographic record
Abstract
from air operations with any identifiable medical risk, and led to frequent medical disqualification. The concept of considering aeromedical risk as part of the spectrum of risks that could lead to aircraft accidents (including mechanical risks and human factors) was first proposed in the 1980s and led to the development of the 1% rule which defines the maximum acceptable risk for an incapacitating medical event as 1% per year (or 1 in 100 person-years) to align with acceptable overall risk in aviation operations. Risk management has subsequently evolved as a formal discipline, incorporating risk assessment as an integral part of the process. Risk assessment is often visualised as a risk matrix, with the level of risk, urgency or action required defined for each cell, and colour-coded as red, amber or green depending on the overall combination of risk and consequence. This manuscript describes an approach to aeromedical risk management which incorporates risk matrices and how they can be used in aeromedical decision-making, while highlighting some of their shortcomings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.013 |
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; both teacher heads agree on what is shown here.
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".