Incidental Findings on MRI Brain Imaging in Pilots from the Canadian White Matter Hyperintensity Study
Bibliographic record
Abstract
BACKGROUND: Incidental neuroanatomical findings are commonly identified during brain MRI completed clinically, for research, or for other purposes, including aircrew screening. Such findings range from benign to pathological and some may be disqualifying for flight duties. We present a series of cases with incidental neuroanatomical findings identified in Royal Canadian Armed Forces (RCAF) aircrew during the Canadian White Matter Hyperintensity research study, and the subsequent aeromedical evaluation undertaken to manage them. Our study group performed 48 brain MRI scans on 42 RCAF pilots and 6 aviation physiology technicians and parajumpers. Participants were men ages 25-70 with a mean age of 39. CASE SERIES: Incidental neuroanatomical findings were detected in four pilots, with six distinct findings (four vascular abnormalities, one arachnoid cyst, and one nonspecific nodule). All cases were asymptomatic. After evaluation of the findings of each case by a medical consortium, including an aeromedical neurologist, all pilots were cleared for ongoing duties with no restrictions. DISCUSSION: The rate and nature of incidental findings in RCAF members in the White Matter Hyperintensity Study is consistent with that found in both the general population and in other military pilot populations. There is no international standard for screening or management of incidental findings; therefore, we recommend an approach that involves a case-by-case evaluation of the findings by a multidisciplinary medical team, with careful identification and consideration for high-risk features. Danho S, Saary J. Incidental findings on MRI brain imaging in pilots from the Canadian White Matter Hyperintensity Study. Aerosp Med Hum Perform. 2025; 96(3):255-259.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".