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Record W4416118274 · doi:10.1136/bmj-2025-086174

Managing costly new drugs to support equitable access: international collaboration and patient engagement to assess value, and managed entry agreements

2025· article· en· W4416118274 on OpenAlexaff
Tarang Sharma, Krista Kruja, Christine Årdal, Ruth Lopert, Jorge Mestre‐Ferrandiz, Simone Boselli, Sabine Vogler

Bibliographic record

VenueBMJ · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsEngineering Link (Canada)
Fundersnot available
KeywordsPublic fundingPublic engagementRules of engagementSustainabilityManaged careAccess to medicines

Abstract

fetched live from OpenAlex

Tarang Sharma and colleagues argue for increased collaboration across public and private sectors—internationally and with patients—in drug assessment and managed entry agreements to support equitable and sustainable access to costly medicines

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)0.230
metaresearch head score (Gemma)0.415
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.230
Threshold uncertainty score0.950

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2300.415
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0030.003
Science and technology studies0.0050.020
Scholarly communication0.0290.030
Open science0.0030.034
Research integrity0.0180.032
Insufficient payload (model declined to judge)0.0110.002

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 head0.287
GPT teacher head0.479
Teacher spread0.192 · 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.

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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

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