A Review and Meta-Analysis of Prostate Cancer Utilities
Bibliographic record
Abstract
BACKGROUND: Health-related quality of life is a key issue in prostate cancer (PC) management. The authors summarized published utilities for common health-related quality of life outcomes of PC and determined how methodological factors affect them. METHODS: In their systematic review, the authors identified 23 articles in English, providing 173 unique utilities for PC health states, each obtained from 2 to 422 respondents. Data were pooled using linear mixed-effects modeling with utilities clustered within the study, weighted by the number of respondents divided by the variance of each utility. RESULTS: In the base model, the estimated utility of the reference case (scenario of a metastatic PC patient with severe sexual symptoms, rated by non-PC patients using time tradeoff) was 0.76. Disease stage, symptom type and severity, source of utility, and scaling method were associated with utility differences of 0.10 to 0.32 (P < 0.05). Utilities from PC patients rating their own health were 0.14 higher than those from the reference case, but utilities from PC patients rating scenarios were lowest. Time tradeoff yielded the highest utilities. Computer administration yielded lower utilities than personal interview (P = 0.02). Neither the scale's high anchor nor study purpose had significant effects on utilities. CONCLUSIONS: This study provides pooled utility estimates for common PC health states and describes how clinical and methodological factors can significantly affect these values. When possible, utility estimates for a modeling application should be derived similarly. Formal data synthesis methods might be useful to researchers integrating utility data from heterogeneous sources. Further exploration of these methods for this purpose is warranted.
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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 | 0.035 | 0.099 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.053 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".