Exploring the “Insight Paradox” in TreatmentResistant Schizophrenia: Correlations Between Dimensions of Insight and Depressive Symptoms in Patients Receiving Clozapine
Bibliographic record
Abstract
Objective: There remains a lack of clarity as to the possible cross talk of insight into illness and depressive symptoms in treatment-resistant schizophrenia. We therefore set our primary aim to evaluate relationship between insight dimensions and depressive symptoms in patients with treatment-resistant schizophrenia receiving clozapine. Methods: were included. We collected sociodemographic variables, scores of insight dimensions (treatment compliance, illness recognition, and symptom relabeling with the Schedule for Assessment of Insight), and depressive symptoms with Calgary Depression Score for Schizophrenia. Linear regression models were used to investigate variables associated with depressive symptoms as the outcome of interest. Results: = 0.121). Conclusion: The treatment compliance part of insight was not one of the significant explanatory variables of depressive symptoms, but it explained the variance in functioning, in contrast to the illness recognition dimension of insight. If our findings were replicated in treatment-resistant schizophrenia, they would suggest that promoting treatment compliance dimension of insight instead of recognition of illness could not increase 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 | 0.004 |
| 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 | 0.001 | 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".