Healthy-year equivalents in major joint replacement. Can patients provide meaningful responses?
Bibliographic record
Abstract
OBJECTIVES: Healthy-years equivalents (HYEs) have been proposed as an evaluative measure with advantages over quality-adjusted life-years (QALYs). The main purpose was to assess the feasibility of eliciting HYEs from patients who have undergone major joint replacement; a secondary objective was to examine relationships with postsurgical health status. METHODS: Pre- and postsurgical reports of perceived comorbidity and current arthritic burden were obtained from 194 patients, using a comorbidity checklist, summary scores from the Western Ontario/McMaster Osteoarthritis Questionnaire (WOMAC), summary scores derived from six Likert scales, and holistic utility scores for the same attributes. After surgery, HYEs for the full across-time health profile were also elicited. RESULTS: All measures of arthritic burden were sensitive to pre/postsurgical changes (p = .0001), and comorbidity scores were stable. Two HYE subgroups emerged. An HYE-invariant subgroup ascribed full HYEs to their profiles, while reporting higher Likert (t = 2.1309; p = .0344) and utility (s = 4.1504; p = .0001) scores for their postsurgical health state. An HYE-variant subgroup reported HYEs that were weakly but significantly (p < .009) correlated with Likert (r = .30), utility (rs = .25), and comorbidity (r = -.26) scores for their postsurgical state. CONCLUSIONS: Our results indicate that patients can understand the HYE assessment procedures and provide interpretable responses. However, a significant proportion reports invariant HYEs that could inflate estimates of the overall mean HYE. Further exploration of the HYEs reported by different clinical and attitudinal populations is needed before widespread adoption of this measure.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".