Real-World Functioning in Patients With Schizophrenia: Beyond Negative and Cognitive Symptoms
Bibliographic record
Abstract
Introduction: Interest in the idea of recovery for certain patients with schizophrenia has been growing over the last decade. Improving symptomatology and functioning is crucial for achieving this. Our study aims to identify those factors that substantially contribute to real-world functioning in these patients. Methods: We carried out a cross-sectional study in stable outpatients with schizophrenia on maintenance antipsychotic monotherapy. Patients: We studied 144 outpatients with schizophrenia (DSM-IV-TR criteria) meeting the following criteria: (1) 18–65 years of age; (2) being clinically stable for at least the previous three months; (3) on maintenance antipsychotic monotherapy (prescriptions ≤ 10 mg olanzapine, ≤200 mg quetiapine, or ≤100 mg levomepromazine as hypnotics were also allowed); and (4) written informed consent. Assessment: We collected information on demographic and clinical variables by using an ad hoc questionnaire. For psychopathology, we employed the Spanish versions of the following psychometric instruments: the Positive and Negative Syndrome Scale (PANSS), the Brief Negative Symptom Scale (BNSS-Sp), and the Calgary Depression Scale (CDS). In addition, cognitive domains were assessed using the Verbal Fluency Test (VFT), the Digit Symbol Substitution Test (DSST), and the Trail Making Test, parts A and B (TMT-A and TMT-B). Finally, we employed the Spanish versions of the University of California San Diego Performance-based Skills Assessment (Sp-UPSA) and the Personal and Social Performance (PSP) for assessing functional capacity and real-world functioning, respectively. Statistical analysis: A forward stepwise regression was conducted by entering those variables significantly associated with PSP total score into the univariate analyses (Student's t-test, ANOVA with Duncan's post-hoc test, or bivariate Pearson correlation). Results: A total of 144 patients; mean age 40 years, 64% males, mean length of illness 12.4 years, PSP total score 54.3. The final model was a significant predictor of real-world functioning [F(7, 131) = 36.371, p < 0.001] and explained 66.0% of the variance. Variables retained in the model: BNSS-Sp abulia, asociality, and blunted affect, PANSS general psychopathology, Sp-UPSA transportation, TMT-B, and heart rate. Conclusion: Our model will contribute to a more efficient and personalized daily clinical practice by assigning specific interventions to each patient based on specific impaired factors in order to improve functioning.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".