Cross-country Comparison in the Evaluation of Evolocumab by Health Technology Assessment Agencies in England, Canada, and Australia
Bibliographic record
Abstract
Evolocumab is a proprotein convertase subtilisin/kexin type 9 inhibitor drug which has shown great treatment effects in the treatment of uncontrolled hypercholesterolemia, particularly elevated low-density lipoprotein cholesterol levels. Due to its significant costs, several health technology assessment agencies (HTA) worldwide have exercised caution in issuing its recommendation across different patient groups. This study attempts to review the processes and compare the approach adopted by the HTA agencies in England (National Institute for Care and Health Excellence [NICE]), Canada (Canadian Agency for Drugs and Technologies in Health [CADTH] Common Drug Review), and Australia (Pharmaceutical Benefits Advisory Committee [PBAC]) in the evaluation of evolocumab. Between July and August 2018, the websites of CADTH, the NICE in England, and the PBAC of the Pharmaceutical Benefits Scheme in Australia were searched for technology appraisal documents pertaining to evolocumab. The search included the initial appraisal, resubmissions, as well as the final recommendation made between 2015 and 2018. Significant variability exists between the recommendations and clinical and economic assessment processes in the evaluation of evolocumab across the three selected HTAs. More collaborative efforts may be required to align the interagency HTAs.
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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 | 0.096 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".