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Record W1964416038 · doi:10.1002/psb.870

The Cancer Drugs Fund: how well is the process working?

2012· article· en· W1964416038 on OpenAlexaff
Alastair Whitington, Paul J. Ross, Stan Kaye, Tim Dean

Bibliographic record

VenuePrescriber · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineGovernment (linguistics)CancerCancer drugsProcess (computing)Interface (matter)Access to medicinesFamily medicineNursingInternal medicineEngineeringComputer sciencePublic health

Abstract

fetched live from OpenAlex

Abstract The Cancer Drugs Fund was initiated in April 2011 as part of a Government scheme to enable patients to access the cancer drugs their doctors think will benefit them. 1 In this Expert Panel Discussion, two oncology specialists and a Cancer Network Director discuss how well the fund is working. Copyright © 2012 Wiley Interface Ltd

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)0.113
metaresearch head score (Gemma)0.294
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.113
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1130.294
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0070.007
Scholarly communication0.0360.014
Open science0.0020.006
Research integrity0.0160.013
Insufficient payload (model declined to judge)0.0160.003

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 head0.046
GPT teacher head0.257
Teacher spread0.211 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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