Severity of psychotic episodes in predicting concurrent depressive and anxiety features in acute phase schizophrenia
Bibliographic record
Abstract
BACKGROUND: Considering that depressive and anxiety symptoms are common in schizophrenia, this study investigated whether the severity of a psychotic episode in an acute phase schizophrenia cohort is predictive of concurrent depressive and anxiety features. METHOD: Fifty one recently hospitalised patients suffering from acute phase schizophrenia participated prospectively in a cross-sectional study. The severity of the psychotic episode, the depressive features and the anxiety features were measured by the Structured Clinical Interview for Positive and Negative Syndrome Scale (SCI-PANSS), the Calgary Depression Scale for Schizophrenia (CDSS), the Hamilton Anxiety Rating Scale (HAM-A) and the Staden Schizophrenia Anxiety Rating Scale (S-SARS). The total SCI-PANSS-scores were adjusted to exclude appropriately the depression or anxiety items contained therein. To examine akathisia as potential confounder, the Barnes Akathisia Scale was also applied. The relationships were examined using linear regressions and paired t-tests were performed between lower and higher scores on the SCI-PANSS. RESULTS: A higher adjusted total SCI-PANSS-score predicted statistically significantly higher scores for depressive features on the CDSS (p < 0.0001) and for anxiety features on the HAM-A (p = 0.05) and the S-SARS (p < 0.0001). The group that scored more or equal to the median (=99) of the adjusted total SCI-PANSS, scored significantly higher (p < 0.0001) on the CDSS, the HAM-A and the S-SARS than the group scoring below it. Akathisia measured distinctly different (p < 0.0001) from both the anxiety measures. CONCLUSION: The study suggests that the severity of a psychotic episode in acute phase schizophrenia predicts the severity of concurrent depressive and anxiety features respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".