Antipsychotic treatment beyond antipsychotics: metacognitive intervention for schizophrenia patients improves delusional symptoms
Bibliographic record
Abstract
BACKGROUND: Although antipsychotic medication still represents the treatment of choice for schizophrenia, its objective impact on symptoms is only in the medium-effect size range and at least 50% of patients discontinue medication in the course of treatment. Hence, clinical researchers are intensively looking for complementary therapeutic options. Metacognitive training for schizophrenia patients (MCT) is a group intervention that seeks to sharpen the awareness of schizophrenia patients on cognitive biases (e.g. jumping to conclusions) that seem to underlie delusion formation and maintenance. The present trial combined group MCT with an individualized cognitive-behavioural therapy-oriented approach entitled individualized metacognitive therapy for psychosis (MCT+) and compared it against an active control. METHOD: A total of 48 patients fulfilling criteria of schizophrenia were randomly allocated to either MCT+ or cognitive remediation (clinical trial NCT01029067). Blind to intervention, both groups were assessed at baseline and 4 weeks later. Psychopathology was assessed using the Positive and Negative Syndrome Scale (PANSS) and the Psychotic Symptom Rating Scales (PSYRATS). Jumping to conclusions was measured using a variant of the beads task. RESULTS: PANSS delusion severity declined significantly in the combined MCT treatment compared with the control condition. PSYRATS delusion conviction as well as jumping to conclusions showed significantly greater improvement in the MCT group. In line with prior studies, treatment adherence and subjective efficacy was excellent for the MCT. CONCLUSIONS: The results suggest that the combination of a cognition-oriented and a symptom-oriented approach ameliorate psychotic symptoms and cognitive biases and represents a promising complementary treatment for schizophrenia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".