Does Antidepressant Treatment Improve Cognition in Older People with Schizophrenia or Schizoaffective Disorder and Comorbid Subsyndromal Depression?
Bibliographic record
Abstract
BACKGROUND: Subsyndromal symptoms of depression (SSD) in patients with schizophrenia are common and clinically important. While treatment of depression in major depressive disorder may partially ameliorate cognitive deficits, the cognitive effects of antidepressant medications in patients with schizophrenia or schizoaffective disorder and SSD are unknown. METHODS: The goal of this study was to assess the impact of SSD and their treatment on cognition in participants with schizophrenia or schizoaffective disorder aged ≥40 years. Participants were randomly assigned to a flexible dose treatment with citalopram or placebo augmentation of their current medication for 12 weeks. An ANCOVA compared improvement in the cognitive composite scores, and a linear model determined the moderation of cognition on treatment effects based on the Hamilton Depression Rating Scale and the Calgary Depression Rating Scale scores between treatment groups. RESULTS: There were no differences between the citalopram and placebo groups in changes in cognition. Baseline cognitive status did not moderate antidepressant treatment response. CONCLUSIONS: Although there are other cogent reasons why SSD in schizophrenia warrant direct intervention, treatment does not substantially affect the level of cognitive functioning. Given the effects of cognitive deficits associated with schizophrenia on functional disability, there remains an ongoing need to identify effective means of directly ameliorating them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".