Reply to Naudet and Colleagues. Cost-Effectiveness in Health Technology Assessment—A Case in Alcohol Dependence
Bibliographic record
Abstract
We would like to thank Drs Naudet, Granger and Braillon for their interest in our work, and for opening a discussion that is of interest here: the consideration of complementary evidence for health technology assessment (HTA) decision-making. By ‘complementary evidence’, we refer to clinical trials, patient’s preference and quality of life, clinical guidance, current clinical practice, and public health, cost-effectiveness and budget impact assessments. As such, it should be understood that a cost-effectiveness analysis, such as the one discussed here, represents an element of evidence that is weighted in relation to a continuum of complementary evidence when assessed by HTA decision makers. Dr Naudet and colleagues have raised several points that we would like to comment on. Firstly, the results and conclusions of the meta-analysis by Palpacuer et al ., (2015) should be interpreted with caution, especially from an HTA decision-making point …
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".