Reimbursement agency requirements for health related quality-of-life data: a case study
Bibliographic record
Abstract
A review was undertaken to identify relevant and appropriate heath-utility estimates for patients with atrial fibrillation who had stroke and to appraise them against the published requirements for several countries' Health Technology Assessment agencies: Australia (Pharmaceutical Benefits Advisory Committee), Canada (Common Drug Review), England and Wales (NICE), Germany (Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen), Scotland (Scottish Medicines Consortium) and Sweden (Tandvårds-och läkemedelsförmånsverket). National agencies have created guidelines to support economic evaluations for their own countries but these guidelines differ. It may be more appropriate for agencies to be concerned primarily with the methodological quality of studies that report utilities rather than identifying local values. As such, we recommend some steps that could be considered when assessing the quality of utility studies in a systematic review or meta-analysis. These steps include considering the methods of utility estimation, model needs, generalizability, sample size and the use of large databases. This may, thus, facilitate consistent and rational Health Technology Assessment decision making.
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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 | 0.139 | 0.294 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| 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".