Cognitive deficits, depressive symptoms, insight, and medication adherence in remitted patients with schizophrenia
Bibliographic record
Abstract
Objective: To study the relationship between cognitive deficits, depressive symptoms, insight, and medication adherence in remitted patients with schizophrenia. Methods: Fifty-four patients aged 18–60 years, with schizophrenia in remission, were evaluated for adherence using the Medication Adherence Rating Scale (MARS), cognitive deficits using the Schizophrenia Cognition Rating Scale (SCoRS), depressive symptoms using the Calgary Depression Scale for Schizophrenia (CDSS), and insight using the Beck Cognitive Insight Scale. Results: Twenty-one (38.9%) patients were found to be nonadherent to their medication. A significant negative correlation was found between MARS total score with SCoRS attention/vigilance (r = −0.28), attitude toward negative side effects of the psychotropic medication with SCoRS total score (r = −0.36), SCoRS attention/vigilance (r = −0.27), verbal learning and memory (r = −0.32), reasoning and problem-solving (r = −0.30), and social cognition (r = −0.28). A significant negative correlation was found between CDSS total score with MARS total score (r = −0.50), medication adherence behavior (r = −0.44), and attitude toward negative side effects of psychotropic medication (r = −0.60). MARS total score significantly positively correlated with years in remission (r = 0.29). Conclusions: Poor medication adherence was seen in more than one-third of remitted patients with schizophrenia and was associated with global cognitive deficits, depressive symptoms, and the number of years in remission.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".