Challenging cognitive cases among physician populations: case vignettes and recommendations
Bibliographic record
Abstract
BACKGROUND: Physicians are not immune to cognitive impairment. Because of the risks created by practising doctors with these issues, some have suggested developing objective, population-specific measures of evaluation and screening guidelines to assess dysfunction. However, there is very little published information from which to construct such resources. AIMS: To highlight the presentation characteristics and provide evaluation recommendations specific to the needs of physicians with actual or presumed cognitive impairment. METHODS: A retrospective database and chart review of cognitively impaired doctors who presented to a physician health programme (PHP). Complex cases were highlighted using simple descriptives and clinical vignettes. RESULTS: A total of 124 cases were included. Clients presented with a variety of issues other than cognitive concerns. We identified four principal domains of impairment: (i) diseases of (or in) the brain (48%); (ii) mood/ anxiety disorders or treatment side effects (28%); (iii) substance use (9%) and (iv) traumatic brain injury (7%). Age was not a good predictor of impairment and brief screening using the Montreal Cognitive Assessment demonstrated a ceiling effect with this cohort. Although many clients underwent some type of professional or personal transition, impairment did not necessarily indicate worse functioning after care. CONCLUSIONS: Physician cognitive evaluations should consider a variety of secondary sources of information, particularly vocational performance reports. It may take time before cognitive impairment can be diagnosed or ruled-out in this population. Prior assumptions, especially for non-cognitive referrals, can lead to inaccurate diagnosis and referrals. PHPs must manage cognitive cases carefully, not only in their clinical complexity but also in their psychosocial aspects.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".