Anaphylaxis: Assessment of a Disease-Based Military Medical Standard
Bibliographic record
Abstract
Although widespread, the use of disease-based employment medical standards is poorly understood or researched. A probabilistic model and threshold value are developed and applied to a military (Canadian Forces [CF]) medical standard for anaphylaxis. Frequency estimates of prevalence, occurrence, and impairing reactions are determined from the literature for military applicants and from medical chart review of military members identified by prescriptions for self-administered epinephrine. The prevalence of prescriptions is 1.13% (CI 1.05, 1.22) and 0.86% (CI 0.72, 1.00) in the CF Regular Force and applicant populations, respectively. The proposed model predicts the annual risk of an impairing allergic reaction in the CF population ranges from 0.1% to 0.16%/year, well below the proposed threshold of 0.5%. The majority of this risk arises from new cases and not recurrences. Requirement for care increases with recurrence. This model allows a useful method of disease-based medical standard review.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".