Accuracy of initial psychiatric diagnoses given by nonpsychiatric physicians
Bibliographic record
Abstract
ABSTRACT: Despite the increased morbidity and mortality associated with psychiatric illnesses, there remains a substantial level of inaccuracy of the initial psychiatric diagnoses given by nonpsychiatric physicians. This study examines the accuracy of initial psychiatric diagnoses by non-psychiatric physicians at the McGill University Health Center (MUHC).We conducted a retrospective chart review for all consultations requested from the consultation-liaison psychiatry service at MUHC. We included all the consultations from January 1, 2018, to December 30, 2018, and excluded patient data with established psychiatric diagnoses. In all requested consults, each diagnosis of a referring physician was compared with the final diagnosis given by the C-L psychiatry team. Conformity between the 2 was validated as accurate.Of the 980 referred inpatients, 875 were enrolled. Patients ranged in age and those older than 70 years constituted the largest group: 54.4% were male. For 467 patients (55.20%), the initial diagnostic impression given by the referring physicians agreed with the final diagnosis made by the C-L psychiatry team, while in 379 patients (44.80%), the initial diagnostic impression was not consistent with the final diagnosis made by the C-L team.Diagnostic impressions of neurocognitive and substance use disorders were highly accurate, but this was not the case when the referring physicians suspected depression or bipolar, personality, or psychotic disorders. This study shows that around half of the referrals were accurately diagnosed, which evinces that nonpsychiatric physicians' knowledge regarding psychiatric conditions is not optimal and that might negatively impact screening and treating these conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".