A systematic literature review of revealed preferences of decision-makers for recommendations of cancer drugs in health technology assessment
Bibliographic record
Abstract
Abstract Objectives This review intends to provide an overview of revealed preferences of decision-makers for recommendations of cancer drugs in health technology assessment (HTA) among the different agencies. Methods A systematic literature search was performed in MEDLINE and EMBASE databases from inception to July 2020. The studies were eligible for inclusion if they conducted a quantitative analysis of HTA’s previous decisions for cancer drugs. The factors with p-values below the significance level of .05 were considered as the statistically significant factors for HTA decisions. Results A total of nine studies for six agencies in Australia, Belgium, France, South Korea, the UK, and Canada were eligible to be included. From the univariable analysis, improvements in clinical outcomes and cost-effectiveness were found as significant factors for the agencies in Belgium, South Korea, and Canada. From the multivariable analysis, cost-effectiveness was found as a positive factor for the agencies in the UK, South Korea, and Canada. Few factors related to characteristics of disease and technology were found to be significant among the included agencies. Conclusions Despite the different drug reimbursement systems and the socioeconomic situations, cost-effectiveness and/or improvement on clinical outcomes seemed to be the most important factors for recommendations of cancer drugs among the agencies.
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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 | 0.027 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".