Affective Symptoms as Prognosis Factor in Schizophrenia
Bibliographic record
Abstract
Schizophrenia has a multidimensional symptomatology that includes affective, aggressive, disorganized, positive and negative clinical manifestations. We selected the affective symptoms as a target for our study because they could have significant impact over the prognosis and quality of patient’s life. To assess the presence of mood symptoms, depressive type and the impact of atypical antipsychotics over these clinical manifestations. This prospective, open label, randomized trial included 36 inpatients, 22 male and 14 female, medium age 25.4 years, diagnosed with schizophrenia according to DSM IV TR criteria that were admitted for acute psychotic de-compensations. During this 12 months trial patients were evaluated using PANSS, CGI-S and CDSS (Calgary Depression Scale for Schizophrenia) every 4 weeks. There were formed 4 equally groups of patients and each group received a different antipsychotic: olanzapine mean daily dose (mdd) 12.7 mg/day, risperidone mdd 5.8 mg/day, aripiprazole mdd 15 mg/day or quetiapine mdd 650mg/day. Patients with depressive symptoms at admission had a poorer prognosis over 12 months (PANSS improvements -22.5+/-4.3 in significant CDSS group defined as score over 6 vs. -29.3+/-2.2 in low CDSS score group, p<0.05). No significant differences in efficacy over depressive symptoms between antipsychotics were recorded at end-point. A number of 3 subjects were discontinued due to lack of compliance and 2 were lost of follow-up. Depressive symptoms are negative prognosis factor and need to be actively addressed and monitored. The four antipsychotics evaluated in this trial were equipotent in decreasing the depressive symptoms severity.
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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 | 0.001 |
| 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) | 0.001 | 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".