<p>Correlation of Health-Related Quality of Life in Clinically Stable Outpatients with Schizophrenia</p>
Bibliographic record
Abstract
BACKGROUND: Generic health-related quality of life (HRQoL) scales are increasingly being used to assess the effects of new treatments in schizophrenia. The objective of this study is to better understand the usefulness of generic and condition specific HRQoL scales in schizophrenia by analyzing their correlates. METHODS: Data formed part of the Pattern study, an international observational study among 1379 outpatients with schizophrenia. Patients were evaluated with the Mini International Neuropsychiatric Inventory, the Clinical Global Impression-Schizophrenia (CGI-SCH) Scale and the Positive and Negative Syndrome Scale (PANSS) and reported their HRQoL using the Schizophrenia Quality of Life Scale (SQLS), the Short Form-36 (SF-36), and the EuroQol-5 Dimension (EQ-5D). The two summary values of the SF-36 (the Mental Component Score and the Physical Component Score, SF-36 MCS and SF-36 PCS) were calculated. RESULTS: Higher PANSS positive dimension ratings were associated with worse HRQoL for the SQLS, EQ-5D VAS, SF-36 MCS, and SF-36 PCS. Higher PANSS negative dimension ratings were associated with worse HRQoL for the EQ-5D VAS, SF-36 MCS, and SF-36 PCS, but not for the SQLS or the EQ-5D tariff. PANSS depression ratings were associated with lower HRQoL in all the scales. There was a high correlation between the HRQoL scales. However, in patients with more severe cognitive/disorganized PANSS symptoms, the SQLS score was relatively higher than the EQ-5D tariff and SF-36 PCS scores. CONCLUSION: This study has shown substantial agreement between three HRQoL scales, being either generic or condition specific. This supports the use of generic HRQoL measures in schizophrenia. CLINICALTRIALSGOV IDENTIFIER: NCT01634542 (July 6, 2012, retrospectively registered).
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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 | 0.005 |
| 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 | 0.001 |
| 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".