Impact of schizophrenia and schizophrenia treatment-related adverse events on quality of life: direct utility elicitation
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of schizophrenia, its treatment and treatment-related adverse events related to antipsychotics, on quality of life from the perspective of schizophrenia patients and laypersons. METHODS: Health state descriptions for stable schizophrenia, extra pyramidal symptoms (EPS), hyperprolactinemia, diabetes, weight gain and relapse were developed based on a review of the literature and expert opinion. The quality of life impact of each health state was elicited using a time trade-off instrument administered by interview to 49 stable schizophrenia patients and 75 laypersons. Regression techniques were employed to examine the importance of subject characteristics on health-related utility scores. RESULTS: Patients and laypersons completed the interview in similar times. Stable schizophrenia had the highest mean utility (0.87 and 0.92 for laypersons and patients respectively), while relapse (0.48 and 0.60) had the lowest mean utility. Of the treatment-related adverse events, EPS had the lowest mean utility (0.57 and 0.72, respectively). Age, gender and PANSS score did not influence the utility results independently of health state. On average, patient utilities are 0.077 points higher than utilities derived from laypersons, although the ranking was similar between the two groups. CONCLUSION: Events associated with schizophrenia and treatment of schizophrenia can bring about a significant detriment in patient quality of life, with relapse having the largest negative impact. Results indicate that patients with stable schizophrenia are less willing to trade years of life to avoid schizophrenia-related symptoms compared to laypersons. Both sets of respondents showed equal ability to complete the questionnaire.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".