Use of forecasted assessment of quality of life to validate time‐trade‐off utilities and a prostate cancer screening decision‐analytic model
Bibliographic record
Abstract
PURPOSE: To determine whether the forecasted assessment of how someone would feel in a future health state can be predictive of utilities (e.g. as elicited by the time-trade-off method) and also predictive of optimal decisions as determined by a decision-analytic model. METHODS: We elicited time-trade-off utilities for prostate cancer treatment outcomes from 168 men. We also elicited forecasted assessments, that is, an informal, non-quantitative, descriptive evaluation, of impotence and incontinence from these men. We used multivariate regression analysis to explore the relationship between forecasted assessment and reluctance to trade length for improved quality of life, that is, the unwillingness to trade length of life for improved quality of life in the time-trade-off utility assessment and the relationship between the forecasted assessments and the optimal decision of whether to undergo screening for prostate cancer as determined from a previously published decision-analytic model. RESULTS: Importance of sexual function was strongly related to impotence utilities (P < 0.05). Based on the multivariate analysis, significant predictors for the utility of severe incontinence were family income, family history of prostate cancer, work status and attitude towards needing to wear an incontinence pad. However, no variables were statistically significant predictors for the utility of complete impotence. The importance of sexual functioning was a significant predictor of the optimal decision. CONCLUSION: Anticipated difficulty adjusting to adverse health effects were highly related to preferences and could be used as a proxy measure of utility. Similarly, the importance of sexual functioning, a future preference, was highly related to the optimal decision, which validates our previously published decision-analytic model.
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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 | 0.019 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".