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Record W2136325259 · doi:10.12927/hcpol.2011.22177

Access to Cancer Drugs in Canada: Looking Beyond Coverage Decisions

2011· article· en· W2136325259 on OpenAlexaffvenueabout
Roger Chafe, Anthony J. Culyer, Mark Dobrow, Peter C. Coyte, Carol Sawka, Kara Laing, Maureen Trudeau, Sharon R. Smith, Jeffrey S. Hoch, Steve Morgan, Stuart Peacock, Rick Abbott, Terrence Sullivan

Bibliographic record

VenueHealthcare policy · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaMemorial University of Newfoundland
Fundersnot available
KeywordsCancer drugsConsistency (knowledge bases)CancerDrugMedicineSelection (genetic algorithm)Family medicineBusinessPharmacologyInternal medicineComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine variation in patients' access to a set of cancer drugs through publicly funded provincial drug programs. DATA SOURCES/STUDY DESIGN: We surveyed provincial drug program managers about their highest-expenditure intravenous and oral cancer drugs. We then investigated whether the same cancer drugs account for the highest expenditures across the provincial programs. We also compared the rates at which these drugs are accessed through these programs. PRINCIPAL FINDINGS: While there is moderate consistency in the selection of cancer drugs that account for the highest provincial expenditures, considerable differences were found in the rates at which some drugs are accessed across provincial programs. CONCLUSIONS: The study demonstrates the existence of interprovincial variation in publicly funded access to cancer drugs even after these drugs have been approved for public coverage.

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)0.002
metaresearch head score (Gemma)0.012
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: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.345

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.009
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.086
GPT teacher head0.320
Teacher spread0.234 · 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

Citations35
Published2011
Admission routes3
Has abstractyes

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