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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".