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Record W2414866897

Cancer Care Ontario's New Drug Funding Program: controlled introduction of expensive anticancer drugs.

2002· article· en· W2414866897 on OpenAlexaffabout
William K. Evans, Marilyn F. Nefsky, Joseph L. Pater, George P. Browman, D. H. Cowan

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsCancer Care Ontario
Fundersnot available
KeywordsMedicineCancer drugsFamily medicineAdvisory committeeDrugFiscal yearCancerPublic administrationFinancePharmacologyBusinessInternal medicinePolitical science
DOInot available

Abstract

fetched live from OpenAlex

In the mid 1990s, the high cost and increasing number of new anticancer and supportive care drugs began to result in an inequality of access to promising new treatment approaches in the Province of Ontario. Starting with a single drug, paclitaxel, in 1995, the New Drug Funding Program has evolved to a provincial program that enables cancer patients in Canada's most populous province to equitably access new and expensive, intravenously administered drugs. This article describes the development of the program, including the evolution of the administrative mechanisms necessary to manage the program and the decisions of the Policy Advisory Committee that shape provincial funding policies. In fiscal year 2000/2001, the Program made 14 drugs available for 24 indications for a total provincial expenditure of approximately $37.7 million. These intravenous drugs can now be accessed through nine Regional Cancer Centres, the province's only cancer hospital (Princess Margaret Hospital) and 80 community hospitals and will directly benefit more than 8,700 patients.

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)0.004
metaresearch head score (Gemma)0.013
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: none
Teacher disagreement score0.943
Threshold uncertainty score0.414

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.002

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 head0.072
GPT teacher head0.270
Teacher spread0.198 · 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

Citations13
Published2002
Admission routes2
Has abstractyes

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