Original Research Recurrence Rates in Ontario Physicians Monitored for Major Depression and Bipolar Disorder
Bibliographic record
Abstract
Objective: Physicians with recurrent conditions that may affect job performance are sometimes referred for monitoring to help ensure compliance with treatment, ongoing remission of illness, and patient safety. Little is known about recurrence rates among doctors monitored for mood disorders. Our primary objective was to describe recurrence rates among Ontario physicians monitored for recurrent unipolar depression and bipolar disorder (BD). Our secondary objective was to explore predictors of recurrence. Method: We used a retrospective cohort design to describe the time to recurrence, defined as either stopping work due to symptoms or any re-emergence of symptoms meeting a pre-established clinical threshold. Our exploratory analysis of recurrence predictors included age, sex, psychiatric diagnosis, psychiatric comorbidity, medical comorbidity, number of past episodes, past hospitalizations, and family history of psychiatric disorder. Results: During a median observation of 24 months, 36 % (of 50) stopped work due to recurrence and 52% (of 50) physicians had a re-emergence of clinical symptoms. The median time to stopping work due to recurrence was 11 months and the median time to any level of symptom re-emergence was 13 months. Physicians with psychiatric comorbidity stopped work sooner (hazard ratio [HR] 3.53; 95 % CI 1.24 to 10.03) and had more rapid symptom re-emergence (HR 2.96; 95 % CI 1.34 to 6.52) than those without comorbidity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".