Air, rail and road: Medical Guidelines for Employees with a History of Cerebrovascular Disease
Bibliographic record
Abstract
Background An acute medical condition following a previous stroke among those who operate trains, airplanes, and commercial vehicles can result in serious accidents. There are guidelines in place to assist physicians and employers in assessing the risks of returning to work after stroke but the extent and comprehensiveness across nations and among safety-critical occupations are not widely known. Methods Medical guidelines currently in place to regulate safety critical occupations including railway engineers, pilots and commercial vehicle drivers were systematically reviewed. Electronic and hand literature searches as well as review of grey literature for Canada, the USA, the UK, and Australia were conducted. Results There is no consistent set of guidelines that address the risk of a second catastrophic event after an initial cerebrovascular event in those employed in safety critical occupations in the four countries assessed. Some broad principles existed between the different countries and occupations but there was major variation in the approach to cerebrovascular disease and its impact on those working in safety-critical occupations. Conclusions A synthesis of current knowledge would assist in establishing risks of a catastrophic event in those who have already suffered from cerebrovascular illness. This will allow the creation of medical guidelines which could be applied to any safety critical occupation in any nation.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".