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Record W2003883102 · doi:10.3821/145.6.cpj246b

Provinces/Territories Join Together to Ask the Federal Government to Delay Approving Generic OxyContin

2012· article· en· W2003883102 on OpenAlexvenueaboutno aff
Kathie Lynas

Bibliographic record

VenueCanadian Pharmacists Journal / Revue des Pharmaciens du Canada · 2012
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)AppealMedicinePolitical scienceFamily medicineLaw

Abstract

fetched live from OpenAlex

The health ministers of Canada's provinces and territories have unanimously agreed to ask for a delay in the approval of a generic form of OxyContin. The ministers made the appeal to the federal government on September 27, 2012, as they met in Halifax to discuss a range of health issues — from federal health care transfer payments to recently announced cuts to the health benefits of some refugees. Nova Scotia Health Minister David Wilson, host of the provincial/territorial gathering, said he and his colleagues agree that further research should be done before any generic versions of the highly addictive and frequently abused drug are approved for the Canadian market. Although OxyContin remains legal in Canada, Ontario and other provinces and territories no longer fund it. The drug's manufacturer, Purdue Pharma, is now marketing OxyNeo as a replacement that is more difficult to grind up to make a powder or liquid for illicit use. The patent for OxyContin expires on November 25, 2012. Health Canada says it has received applications for generic versions. The provincial/territorial ministers presented their unanimous request to federal health minister Leona Aglukkaq, when she joined their meeting on September 28. Health Canada's decisions on drug approvals aren't based on politicians' views, she said at a news conference.

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)0.010
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: Editorial · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.358

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0160.003
Scholarly communication0.0050.001
Open science0.0020.002
Research integrity0.0140.012
Insufficient payload (model declined to judge)0.0240.003

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 head0.025
GPT teacher head0.264
Teacher spread0.239 · 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
GenreEditorial

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 routes2
Has abstractyes

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