Designing economic evaluations to facilitate optimal decisions: the need to avoid bias
Bibliographic record
Abstract
Karen M Lee,1,3 Kathryn Coyle,2 Doug Coyle2,31Canadian Agency for Drugs and Technologies in Health (CADTH), Ottawa, ON, Canada; 2Health Economics Research Group, Brunel University, Uxbridge, UK; 3School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, ON, CanadaGuertin et al1 argue in their article “Bias within economic evaluations” that if researchersfail to incorporate the future availability of generics entrants for new patented drugs, the incremental cost-effectiveness ratio (ICER) will be overestimated.1 Before addressing the validity of this argument, it is first worthwhile to consider the nature of both bias and economic evaluation.Read the original paper by Guertin et al
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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 | 0.632 | 0.864 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".