Citalopram in first episode schizophrenia: The DECIFER trial
Bibliographic record
Abstract
Antidepressants are frequently prescribed in first episode schizophrenia (FES) patients for negative symptoms or for subsyndromal depressive symptoms, but therapeutic benefit has not been established, despite evidence of efficacy in later-stage schizophrenia. We conducted a 52 week, placebo-controlled add-on trial of citalopram in patients with FES who did not meet criteria for major depression to determine whether maintenance therapy with citalopram would improve outcomes by preventing or improving negative and depressive symptoms. Primary outcomes were negative symptoms measured by the Scale for Assessment of Negative Symptoms and depressive symptoms measured by the Calgary Depression Scale for Schizophrenia; both were analyzed by an intent-to-treat, mixed effects, area-under-the-curve analysis to assess the cumulative effects of symptom improvement and symptom prevention over a one-year period. Ninety-five patients were randomized and 52 (54%) completed the trial. Negative symptoms were reduced with citalopram compared to placebo (p = .04); the effect size of citalopram versus placebo was 0.32 for participants with a duration of untreated psychosis (DUP) of <18 weeks (median split) and 0.52 with a DUP >18 weeks. Rates of new-onset depression did not differ between groups; improvement in depressive symptoms was greater with placebo than citalopram (p = .02). Sexual side effects were more common with citalopram, but overall treatment-emergent side effects were not increased compared to placebo. In conclusion, citalopram may reduce levels of negative symptoms, particularly in patients with longer DUP, but we found no evidence of benefit for subsyndromal depressive symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".