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Record W2155761001 · doi:10.1345/aph.1k373

Drug Reimbursement Policies in Canada—Need for Improved Access to Critical Therapies

2008· article· en· W2155761001 on OpenAlexaffabout
Jacques LeLorier, Alan Bell, David Bougher, Jafna L. Cox, Alexander GG Turpie

Bibliographic record

VenueAnnals of Pharmacotherapy · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsMcMaster UniversityHamilton Health SciencesDalhousie UniversityHumber River Regional HospitalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsReimbursementMedicinePrior authorizationAuthorizationMultidisciplinary approachPublic healthDrugIntensive care medicineHealth careNursingEconomic growthPharmacology

Abstract

fetched live from OpenAlex

Public drug programs in Canada are increasingly implementing cost management strategies. A multidisciplinary review of these strategies--specifically, the special authorization (SA) process--found that implementation of the SA practice is costly and causes inequity in access, underutilization, and delays in treatment for urgently required therapies, all potentially leading to negative health outcomes. We present potential solutions and a set of recommendations for decision-makers to base reimbursement decisions on the best clinical evidence, eliminate regional variability in access, ensure timely access to urgently required treatments, and monitor the impact of reimbursement policies on health outcomes.

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)0.005
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score0.669

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0030.002
Scholarly communication0.0060.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.560
GPT teacher head0.540
Teacher spread0.021 · 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 designNot applicable
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

Citations12
Published2008
Admission routes2
Has abstractyes

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