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Record W2071885854 · doi:10.1136/bmj.38582.703866.ae

“Breakthrough” drugs and growth in expenditure on prescription drugs in Canada

2005· article· en· W2071885854 on OpenAlexaffabout
Steven G. Morgan, Kenneth Bassett, James M Wright, Robert G. Evans, Morris L. Barer, Patrícia Caetano, Charlyn Black

Bibliographic record

VenueBMJ · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsVancouver Hospital and Health Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsMedical prescriptionMedicineDrug industryComputer scienceData sciencePharmacologyBiologyBiotechnology

Abstract

fetched live from OpenAlex

Driven by increased use of prescription drugs and by shifts from old to new products, spending on drugs in Canada doubled between 1996 and 2003. 1 Which drugs drove this expenditure growth? The Canadian Patented Medicine Prices Review Board appraises the therapeutic novelty of every patented medicine in Canada to distinguish "breakthrough" drugs from other medicines. Since 1990, the board has published these appraisals in annual reports. 2 We applied the board's classifications for breakthrough drugs to total expenditures on and use of prescription drugs in the province of British Columbia (population 4.2 million).

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)0.001
metaresearch head score (Gemma)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.865

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.013
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.030
GPT teacher head0.268
Teacher spread0.238 · 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

Citations107
Published2005
Admission routes2
Has abstractyes

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