M122. DEPRESSION IN SCHIZOPHRENIA SPECTRUM DISORDERS: LONGITUDINAL COURSE AND THE RELATIONSHIP WITH OTHER CLINICAL PARAMETERS AND QUALITY OF LIFE
Bibliographic record
Abstract
Abstract Background The relationship between schizophrenia and depression is complex. Longitudinal studies on the course of depression in first episode schizophrenia populations are scarce and there are conflicting results on the predictive value of some baseline measures. Methods We conducted an open label longitudinal cohort study which included 126 patients with first-episode schizophrenia spectrum disorders treated with long-acting antipsychotic medication over 24 months. Depression was assessed at three monthly intervals using the Calgary Depression Scale for Schizophrenia. Changes in depression over time were assessed using the linear mixed-effect models for continuous repeated measures. The relationship between depression and other clinical parameters was assessed with regression models. Results Depressive symptoms were most prominent at baseline and showed highly significant reductions in the first three months (p<0.0001). Majority of the patients with depression improved with antipsychotic medication alone and we found associations between depressive symptoms with insight and poorer quality of life, however only illness awareness (p=0.0035) was the only significant predictor on depression in our regression analysis. There were a few differences between patients who experienced depression during the acute phase of treatment and those in the post-acute phase. Discussion Our findings suggest that depression in schizophrenia is common and generally responds well to treatment. The relationship between depression and insight has implications for further treatment considerations
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".