Prevalence and clinical correlates of depression in the acute phase of first episode schizophrenia
Bibliographic record
Abstract
BACKGROUND: Reported rates of depression in schizophrenia vary considerably. OBJECTIVE: To measure the prevalence of depression in a first episode sample of people with schizophrenia. METHODS: All referrals with a first episode of schizophrenia diagnosed using SCID interviews were assessed pre-discharge and again six months later. We used the Calgary Depression Scale for Schizophrenia (CDSS) and Positive and Negative Syndrome Scale (PANSS) to assess the severity of symptoms. RESULTS: Pre-discharge, 10.4% of the sample met CDSS criteria for depression. According to the PANSS depression (PANSS -D) subscale, 3% of patients were depressed, with a mean score of 7.48 (SD = 2.97). Only 3% of patients pre-discharge were found to be depressed on both the CDSS and the PANSS-D. Six months later 6.5% were depressed according to the CDSS. However none reached depression criteria according to the PANSS-D. The CDSS correlated with PANSS-D both pre-discharge and at follow-up. Feelings of depression and self-deprecation were the most common symptoms at baseline and follow-up. The CDSS was unrelated to negative symptoms at both stages. A lifetime history of alcohol abuse increased the risk for depression. CONCLUSION: Rates of depression in this sample were low. The CDSS appears to discriminate between depression and negative symptoms. Like the general population, alcohol misuse is a risk factor for depression in first episode schizophrenia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".