Positive Symptoms Are Associated With Clinicians’ Global Impression in Treatment-Resistant Schizophrenia
Bibliographic record
Abstract
Previous investigations on the relationship between global rating measures and symptoms have not considered the additional role of functioning. In this naturalistic study, we examined the relationship between symptom domains and functioning on Clinical Global Impression scales for severity (CGI-S) and improvement (CGI-I) in a sample of patients with schizophrenia assessed to be treatment resistant. Participants were patients with a diagnosis of schizophrenia or schizoaffective disorder who failed 2 prior antipsychotic trials and were considered candidates for clozapine. They were assessed on the 18-item Brief Psychiatric rating Scale (BPRS), Social Occupational Functioning Assessment Scale (SOFAS), and CGI-S at baseline. A subset of patients was followed up at 6 weeks after initiation of clozapine and assessed on the CGI-I. The independent effects of symptom domains and functioning on the CGI scales were examined via multivariate regression models. Brief Psychiatric rating Scale positive factor (P < 0.001) and SOFAS (P < 0.001) scores were significant determinants of CGI-S at baseline. Multivariate models suggested that relative change measures had a better fit for the CGI-I compared to absolute change measures (R = 0.72 vs R = 0.61, respectively). Improvements in BPRS positive (P < 0.001) and affect (P = 0.002) factors and SOFAS (P = 0.030) scores were significant determinants of CGI-I. Ratings of 1 and 2 on the CGI-I corresponded to a mean relative change in the BPRS total of 65% and 41%, respectively. Positive symptoms were a key determinant of clinicians' impression of severity and improvement in this study. Although psychosocial functioning played a large part in determining severity, it was not as significant in the assessment of improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".