MétaCan
Menu
Back to cohort
Record W2997817159 · doi:10.4103/hm.hm_17_19

Cross-country Comparison in the Evaluation of Evolocumab by Health Technology Assessment Agencies in England, Canada, and Australia

2019· article· en· W2997817159 on OpenAlexaboutno aff
Swaroop Varghese, Marc‐Alexander Ohlow, Narendra Kumar

Bibliographic record

VenueHeart and Mind · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsEvolocumabNiceExcellenceAgency (philosophy)MedicineAdvisory committeePolitical scienceFamily medicinePublic administrationSociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.289
GPT teacher head0.485
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueHeart and MindSame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207