What Should Be Reported in a Methods Section on Utility Assessment?
Bibliographic record
Abstract
BACKGROUND: The measurement of utilities, or preferences, for health states may be affected by the technique used. Unfortunately, in papers reporting utilities, it is often difficult to infer how the utility measurement was carried out. PURPOSE: To present a list of components that, when described, provide sufficient detail of the utility assessment. METHODS: An initial list was prepared by one of the authors. A panel of 8 experts was formed to add additional components. The components were drawn from 6 clusters that focus on the design of the study, the administration procedure, the health state descriptions, the description of the utility assessment method, the description of the indifference procedure, and the use of visual aids or software programs. The list was updated and redistributed among a total of 14 experts, and the components were judged for their importance of being mentioned in a Methods section. RESULTS: More than 40 components were generated. Ten components were identified as necessary to include even in an article not focusing on utility measurement: how utility questions were administered, how health states were described, which utility assessment method(s) was used, the response and completion rates, specification of the duration of the health states, which software program (if any) was used, the description of the worst health state (lower anchor of the scale), whether a matching or choice indifference search procedure was used, when the assessment was conducted relative to treatment, and which (if any) visual aids were used. The interjudge reliability was satisfactory (Cronbach's alpha = 0.85). DISCUSSION: The list of components important for utility papers may be used in various ways, for instance, as a checklist while writing, reviewing, or reading a Methods section or while designing experiments. Guidelines are provided for a few components.
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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 | 0.206 | 0.540 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.067 | 0.027 |
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".