Bibliographic record
Abstract
Practitioners and policy-makers have a common interest in an instrument that will measure the value people place on different health states. For practitioners, a patient’s valuation can guide individual decisions. For senior managers and policy-makers the information can help them decide which services to provide and which technologies to install. It is therefore disappointing to encounter results, such as those from the Leung study in this issue of The Journal 1, that show 2 reputable and widely used instruments that purport to provide such information giving different results. It raises a number of questions: How can such an outcome arise? What should practitioners and policy-makers do in view of such a finding? Measuring patient quality of life (QOL) is a relatively new venture — in particular, measurement using multi-attribute utility (MAU) instruments, like those used in the Leung study. MAU are questionnaires in which response categories are scored using utility weights that purport to measure the strength of preference for a health state. In principle, the application of these weights permits comparison of dissimilar health states. A higher score for health state A versus health state B does not have clinical meaning. Rather, a higher score indicates that there is a subjective preference for health state A by the person or persons whose judgment was used to create the utility weights. MAU instruments are now widely employed in the calculation of quality-adjusted life-years (QALY) or, more correctly, preference-adjusted life-years. Used in economic evaluation studies, QALY serve to rank dissimilar services (cost utility analysis). A variant of the QALY — the disability-adjusted life-year — is used to estimate the burden of disease (for example, the recent World Health Organization Burden of Disease Study2). The field is currently dominated by a limited number of MAU instruments. … Address correspondence to N.A. Day; E-mail: neilathertonday{at}bigpond.com
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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 | 0.585 | 0.832 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".