MétaCan
Menu
Back to cohort
Record W1447107852 · doi:10.3233/jrs-140619

Investigating pharmaceutical marketing in Canada using American prosecutions

2014· article· en· W1447107852 on OpenAlexaffabout
Rami Shoucri, Navindra Persaud

Bibliographic record

VenueInternational Journal of Risk & Safety in Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsSanctionsPromotion (chess)BusinessLiberian dollarPharmaceutical industryPharmaceutical marketingJurisdictionLicensureLawPolitical scienceFinanceMedicinePharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: Pharmaceutical companies are prohibited from marketing medications for off-label uses in both the United States and Canada. In the United States, there have been several recent multi-billion dollar settlements with pharmaceutical companies based, partly, on off-label promotion. Health Canada has not publicized any investigations into, or prosecutions of, pharmaceutical companies for off-label promotion in Canada even though many of the same medications are marketed here. The prohibition on off-label promotion is largely directed at preventing pharmaceutical companies from circumventing the drug licensing process and attendant safety checks. OBJECTIVE: To determine if sanctions for off-label pharmaceutical promotion in one jurisdiction can be used to regulate marketing in another. METHODS: We reviewed and compared the laws and regulatory bodies in Canada and the United States to determine if Canadian regulators could use the findings of American regulators. RESULTS: There were no important differences in the laws and regulatory bodies in Canada and the United States related to off-label promotion. CONCLUSIONS: Canadian regulators can use the findings of American regulators to investigate off-label promotion in Canada. All countries should consider using sanctions in other jurisdictions to direct the deployment of limited regulatory resources.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.405
Teacher spread0.357 · 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 teacher head, not a consensus.

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

Citations0
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Risk & Safety in MedicineSame topicPharmaceutical studies and practicesFrench-language works237,207