Relationship Between Symptomatic Improvement and Overall Illness Severity in Patients With Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Whether improvement on ratings of global illness severity is differentially associated with improvement in specific symptom domains in patients with schizophrenia is not well understood. The present study examined the independent relationships between improvement in specific symptom clusters and change in global impressions of illness severity. METHODS: This study included 589 patients with chronic schizophrenia who were assessed at baseline and after 6 months of antipsychotic treatment. Both clinicians and patients completed the Clinical Global Impressions-Severity of Illness Scale (CGI-S). Symptom severity was assessed using factor scores derived from the Positive and Negative Syndrome Scale. RESULTS: Change in illness severity ratings made by the clinician and those made by the patient demonstrated moderate overlap. Nearly half of the patients were evaluated as clinically improved during the 6-month period, as rated by the clinician, with less than a third of patients experiencing a reduction in illness severity as determined by both the clinician and themselves. Improvements in clinician-rated CGI-S scores were most strongly associated with reduction in positive symptom severity. In contrast, change in patient-rated CGI-S scores was not linked to reduction in positive symptoms but rather to improvement in depressive and anxiety symptoms. This latter finding remained in a subsample of patients with relatively preserved insight into illness, suggesting that lack of insight cannot account for these findings. Finally, reduction in positive symptoms beyond 2 to 3 points was found to be clinically meaningful. CONCLUSIONS: In conclusion, change in overall illness severity, as determined by clinicians, is not necessarily interchangeable with patients' view of improvement of their own clinical status. Moreover, changes in the 2 evaluations of illness severity are associated with changes in different symptom domains.
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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 | 0.006 |
| 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 | 0.001 |
| 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".