116 Ct coronary angiography versus coronary artery calcium scoring for the occupational assessment of military aircrew
Bibliographic record
Abstract
Introduction To ensure flight safety military aircrew undergo regular clinical and occupational assessment. Coronary artery calcium scoring (CACS) is established as an imaging modality to non-invasively assess coronary artery disease (CAD). CT coronary angiography (CTCA) potentially offers a more accurate assessment of CAD but has not been formally assessed in military aircrew. This retrospective cohort study is designed to compare the theoretical differences in downstream investigations and occupational outcomes in aircrew with suspected CAD comparing CTCA with existing CACS pathways. Methods A 2 year retrospective cohort study of consecutive UK military patients who underwent a CTCA and CACS. Patient demographics, CTCA and CACS results and initial and final occupational restrictions were analysed comparing current UK, Canadian and US pathways. Results 44 patients underwent CACS and CTCA. The commonest indication for a CTCA was a positive exercise ECG. Increasing CACS, stenosis severity and stenosis burden were associated with significantly greater likelihood of occupational restriction (p=<0.01). Following CTCA (26/44, 59%) of patients were found to have evidence of CAD with (13/44, 30%) having at least a single vessel stenosis 50%. All of these patients had subsequent occupational restrictions. Two patients with a calcium score 10 had at least one single vessel stenosis 50%. Conclusion A CTCA pathway is potentially a better discriminator of CAD burden in aircrew when compared with CACS and may reduce downstream testing, allowing a more efficacious approach to CAD assessment in military aircrew.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".