Morbidity among airline pilots: the AMAS experience. Aviation Medicine Advisory Service.
Bibliographic record
Abstract
BACKGROUND: Various cohort studies, military databases, and Federal Aviation Administration databases have characterized morbidity and disability in pilots. However, an overriding limitation of these studies is acquiring complete and accurate medical information from pilots with a profession, hobby, or aircraft investment to protect (6). The unique role of Aviation Medicine Advisory Service (AMAS) as pure pilot advocate with guaranteed patient confidentiality eliminates the aviator's need to conceal medical problems. Therefore, analyses of cases reported to AMAS might provide additional insight regarding the true prevalence of morbidity in airline pilots. METHODS: All AMAS cases of airline pilots and flight engineers from January 1996 through November 1999 were reviewed (n = 20,522). During that time, AMAS provided consultation to approximately 51 U.S. and Canadian airlines. Diagnoses were stratified by decades ranging from 20 to 69 yr of age. RESULTS: Notably, the five conditions most frequently inquired about at AMAS were similar to the major causes of long term disability found in a cohort of Air Canada pilots (5). Cardiovascular conditions accounted for almost 25% of the inquiries. However, the relative percentage especially in the older population was less than that reported previously. Interestingly, orthopedic and musculoskeletal cases (10-11%) rated second only to cardiovascular cases. CONCLUSIONS: These findings are limited by the inability to draw an exact reference population at risk, the use of proportional measures for description and the inherent difficulty in attempting to utilize an administrative index as an epidemiological tool. Further study addressing the impact of aviator nondisclosure of medical problems on the reported prevalence of disease among U.S. airline pilots may help target preventive efforts in the future.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".