The Association Between Cognitive Deficits and Clinical Characteristic in First-Episode Drug Naïve Patients With Schizophrenia
Bibliographic record
Abstract
Background: Schizophrenia is a severe mental disease which characterized by positive symptom, negative symptom, general pathology syndrome and cognitive deficits. In recent years, many studies have investigated the relationship between cognitive deficits and clinical characteristics in schizophrenia, but relatively few studies have been performed on first-episode drug-naïve patients. Methods: Eighty seven first-episode drug-naïve schizophrenia patients were assessed for positive symptom, negative symptom, general pathology symptom and cognitive deficits from the Positive and Negative Symptom Scale and MATRICS Consensus Cognitive Battery. Psychotics depression were assessed using the Calgary depressing scale for schizophrenia. The relationship between clinical characteristics and cognitive deficits were assessed using correlation analysis and linear regression analysis. Results: The prevalence of cognitive deficits among the patients in our study was 85.1% (74/87) which was much higher than that in the general population. According to correlation analysis, negative symptom was negatively correlated with speed of processing and social cognition, and general pathology showed a negative correlation with attention/vigilance. In addition, a positive correlation was found between age and speed of processing. No correlation was found between cognitive deficits and positive symptom. Conclusions: This study confirmed that negative symptom is negatively related with some domains of cognitive function in first-episode drug naïve schizophrenia patients. Trail Registration: NCT02880462. Registered August 26, 2016.
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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 | 0.002 |
| 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 | 0.000 |
| 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".