Establishing disability weights from pairwise comparisons for a US burden of disease study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To determine valid and reliable disability weights for a U.S. burden of disease study, a convenience sample of 68 clinical experts was recruited, including representatives from over 20 NIH institutes and Centers for Disease Control and Prevention. Experts were given various health state valuation tasks including pairwise comparison, ranking, and Person Trade Off. Materials consisted of standardized descriptions of 11 attributes per health state (Classification and Measurement System of Functional Health, CLAMES). Attributes comprised up to 5 ordinal levels of disability. All states were displayed either with or without health state labels. Health state descriptions were taken from an existing comprehensive Canadian system. Conditional Logistic (CLR) and Probit Regression (PR) were used to derive disability weights. CLR and PR converged in yielding stable regression weights to construct disability weights, with a correlation of 0.816. The overall test-retest reliability amounted to 92.5% identical decisions. No significant difference was found for the presentation of health states with or without labels. A comparison of the expert valuations from our study with a standard gamble based valuation in the general population resulted in agreement of r = 0.61. The chosen methodology yielded valid and reliable and disability weights. As it is based on a modularized set of attributes, this methodology will allow derivation of disability weights on the basis of existing descriptions using the CLAMES.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.102 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it