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Record W2060894663 · doi:10.1038/clpt.2014.145

Accelerated Access to Innovative Medicines for Patients in Need

2014· article· en· W2060894663 on OpenAlexaff
L G Baird, Reiner Banken, H‐G Eichler, Finn Børlum Kristensen, D K Lee, John C. W. Lim, Robyn Lim, Carole Longson, Edmund J. Pezalla, Tomas Salmonson, Dima Samaha, Sean Tunis, Janet Woodcock, Gigi Hirsch

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsHealth CanadaInstitut National d'Excellence en Santé et en Services Sociaux
FundersInnovative Medicines InitiativeNational Comprehensive Cancer NetworkEuropean Federation of Pharmaceutical Industries and AssociationsBill and Melinda Gates Foundation
KeywordsClinical pharmacologyVariety (cybernetics)Access to medicinesBusinessHealth careMedicinePharmacologyEconomic growthComputer scienceEconomicsNursingPublic health

Abstract

fetched live from OpenAlex

There is broad agreement among health-care stakeholders that more must be done to ensure that patients have timely access to new and innovative medicines. Assuming that industry will continue to develop such medicines at a sustainable rate, regulators and payers become the gatekeepers. Regulators, starting in the late 1980s/early 1990s, and, more recently, payers have implemented a variety of early-access pathways or initiatives, and this practice is continuing even today. This article describes the specific approaches that have been taken in four economically developed regions, reviews their success rates, and suggests possible new directions.

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)0.003
metaresearch head score (Gemma)0.014
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: Commentary · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0290.002

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 head0.272
GPT teacher head0.473
Teacher spread0.201 · 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
GenreCommentary

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

Citations87
Published2014
Admission routes1
Has abstractyes

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