Clinical aspects of super-refractory schizophrenia: a 6-month cohort observational study
Bibliographic record
Abstract
OBJECTIVE: Approximately 30% of treatment-resistant schizophrenic patients do not fully respond to Clozapine and such patients are termed Clozapine non-responders or super-refractory schizophrenics. The aim of this study was to characterize patients with super-refractory schizophrenia according to demographic and psychopathological variables, as compared with patients with refractory schizophrenia or non-refractory subjects. METHOD: One hundred two outpatients meeting DSM-IV criteria for schizophrenia were followed-up for 6 months. Subjects were classified into 3 groups: non-refractory (n=25), refractory (n=43) and super-refractory (n=34). Psychopathology was assessed by the Positive and Negative Syndrome Scale, the Schedule for Deficit Syndrome, the Calgary Depression Scale and the Quality of Life Scale. Patients were rated at 2-month intervals. RESULTS: Higher levels of severity at the disease onset as well as higher severity of positive symptoms were found to be predictive of super-refractoriness. CONCLUSIONS: The super-refractory schizophrenia patients have psychopathological predictive factors that need studies comparing brain images, genetical features and other clinical comparisons.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".