An Off-the-Shelf Help List
Bibliographic record
Abstract
PURPOSE: The Panel on Cost-Effectiveness in Health and Medicine recommends an organized collection of preference measure values for health states that can be used in costutility analyses (CUAs). The authors sought to construct a catalog of preference scores from published CUAs, organize the catalog by clinical categories, and identify methods of preference score assessment. METHOD: The authors systematically searched Medline and other databases to identify original CUAs published through 1997. Information was abstracted on the health state descriptions, corresponding preference scores, method of preference score elicitation, and the source of the estimate. RESULTS: Two hundred twenty-eight CUAs were appraised. The authors found 949 health states and corresponding preference scores. Most frequently, health states pertained to the circulatory system (21.7%), health states were valued by experts (35.8%), and values were derived through community-based preference scores (23.5%). CONCLUSION: A catalog of preference scores for health states can be constructed. The catalog (http://www.hsph.harvard.edu/organizations/hcra/cuadatabase/ intro.html) may provide a useful reference tool for producers and consumers of CUAs but also underscores the methodologic variation and inconsistencies present in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.622 | 0.491 |
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".