Pitfalls of prioritizing cost-effectiveness in the assessment of medical innovation: A comment on Wallis and Detsky guest editorial
Bibliographic record
Abstract
We read with interest Dr Wallis and Dr Detsky (W&D)'s comments on Health Quality Ontario (HQO)'s report on Robotic Assisted Radical Prostatectomy (RARP).1,2 We share the authors' concerns with regard to the quality of the base case costeffectiveness analysis (CEA) included in the report.The pivotal role of a single trial in parameterizing the model, the shortness of the time horizon, and the inadequate consideration of uncertainty in the evidence base are all highly problematic.However, we are also concerned that W&D go on to promote further erroneous approaches to the economic evaluation of health technologies.High quality health care resource allocation processes require the use of the best available evidence.Unfortunately, W&D's suggestions would exacerbate many of the problems with HQO's analysis rather than remedy them.
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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 | 0.090 | 0.268 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.059 | 0.079 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".