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Record W2127309690 · doi:10.1377/hlthaff.25.2.337

Centralized Drug Review Processes In Australia, Canada, New Zealand, And The United Kingdom

2006· article· en· W2127309690 on OpenAlexaffabout
Steven G. Morgan, Meghan McMahon, Craig Mitton, Elizabeth E. Roughead, Ray Kirk, Panos Kanavos, Devidas Menon

Bibliographic record

VenueHealth Affairs · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of British Columbia HospitalUniversity of British Columbia
Fundersnot available
KeywordsTransparency (behavior)Public economicsRigourProcess (computing)Political scienceEconomicsComputer scienceLaw

Abstract

fetched live from OpenAlex

Many countries have centralized the clinical and economic assessments necessary for evidence-based drug coverage policy. We analyze such processes in Australia, Canada, New Zealand, and the United Kingdom. These countries apply comparable approaches to the assessment and appraisal of evidence but apply the processes to different types of drugs and use the reviews within different decision-making contexts. Review processes applied to all medicines and clearly tied to coverage decisions appear to influence national drug use. Rigor of process and transparency of data and rationale are believed to be important for maximizing the impact and political acceptability of the processes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.091
metaresearch head score (Gemma)0.197
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.669

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0910.197
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.007
Science and technology studies0.0060.006
Scholarly communication0.0100.004
Open science0.0020.006
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.220
GPT teacher head0.401
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), 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

Citations186
Published2006
Admission routes2
Has abstractyes

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