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 of view. This meta-analysis did not consider the European Medicines Agency (EMA) indication for treatment with nalmefene. Nalmefene is indicated for reduction of alcohol consumption in adult patients with alcohol dependence who have a high drinking-risk level (DRL), who do not have physical withdrawal symptoms and who do not require immediate detoxification. Nalmefene should be initiated only in patients who continue to have a high DRL 2 weeks after initial assessment. The meta-analysis by Palpacuer and colleagues did not consider only patients within nalmefene’s indication but also those with low and medium DRLs at treatment initiation. Having evaluated different populations on the basis of the nalmefene clinical trials’ data, the EMA identified patients with high and very high DRLs as the optimal population for treatment with nalmefene, representing greater public health benefits and better investments of public resources from a healthcare payer point of view than the wider population considered in the meta-analysis by Palpacuer and colleagues.
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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 | 0.025 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.040 | 0.072 |
| Insufficient payload (model declined to judge) | 0.004 | 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".