The Silence in Hoch et al.’s Commentary about the Rationale for and Objective(s) of Canada’s Separate HTA Process for Cancer Drugs: The Importance of Transparency and Accountability when Allocating Taxpayers’ Dollars
Bibliographic record
Abstract
In their critique of our paper, Hoch et al. [1] ask if it matters whether Canada’s separate health technology assessment (HTA) process for cancer drugs has an economic rationale. They say that the answer is no. However, as we will show later, on the basis of their actual comments, there seems to be ambiguity in what this ‘no’ really means. Hoch et al. also suggest that there were other rationales and objectives set for the establishment of a separate HTA review process for cancer drugs. Unfortunately, they do not identify or explore what these other rationales and objectives might have been, nor do they discuss what these are now and moving forward. Hence, we are left in the dark as to what these might be. Their response reminds us of the famous quotation (above) from Alice in Wonderland. We are left not really knowing where the authors want to go and why. Since we do not know what alternative rationale and objective(s) the authors have in mind, we focus the remainder of our response on comments that we think relate to the subject of our publication (i.e. whether an economic rationale has been provided for the establishment of a separate reimbursement review process for cancer drugs). Specifically, we address the following three issues. First, we discuss whether Hoch et al. really think that an economic rationale is not ‘‘needed to justify the creation and adoption of a separate cancer drug HTA process’’ and what appears to be confusion in their comments between an economic rationale (referred to in our paper as ‘‘a rationale that is consistent with an economic perspective’’ [2]) and goal(s) (or objectives) to achieve through allocation of resources (e.g. to improve population health). Second, we address Hoch et al.’s suggestion that ‘‘decision makers may be pursuing different objectives from academic researchers.’’ Third, we discuss Hoch et al.’s prescription for how to make ‘‘positive contributions that help decision makers address the challenges that decision makers have.’’
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".