Early Psychosis Symptoms Noted by Family Physicians in Electronic Medical Records During Help-Seeking Visits in Primary Care: Symptômes précoces de psychose relevés par les médecins généralistes dans les dossiers médicaux électroniques lors de consultations en soins primaires pour demande d’aide
Bibliographic record
Abstract
Background The objectives of this study were (1) to describe the symptoms noted by family physicians during help-seeking visits for early psychosis, relative to a validated screening tool for early psychosis in primary care, and (2) to examine the referral disposition of patients meeting the screening tool cut-off. Methods We constructed a retrospective cohort of Ontario residents aged 14–35 years with an incident diagnosis of non-affective psychotic disorder between 2005–2015 in health administrative data, and at least one visit in the Electronic Medical Record Primary Care database during the 6 months prior to the date of psychotic disorder diagnosis ( n = 572). We abstracted symptoms of psychosis noted by the family physician in the electronic medical records and compared these to the Primary Care Checklist (PCCL) for early psychosis. Results The most frequent PCCL items noted were “tension or nervousness” (13.3%), “depressive mood” (12.5%), “increased stress or deterioration in functioning” (7.5%), and “sleep difficulties” (6.6%). The PCCL cut-off was met by 187 patients (33%) across 327 visits (8%). A greater proportion of visits meeting the PCCL cut-off had psychosis noted as the main presenting issue (55.4% vs. 6.8%) and resulted in referral to mental health services (33.3% vs. 6.0%) than those not meeting the cut-off. However, two in three visits where the screening cut-off for early psychosis was met did not result in a referral to mental health services. Discussion The findings of this study suggest that family physicians may benefit from a screening tool when early psychosis is suspected to improve identification and guide referral practices.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".