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Record W3164318960 · doi:10.3233/jrs-210013

Companies’ statements about drugs withdrawn from the Canadian market: A descriptive analysis

2021· article· en· W3164318960 on OpenAlexaffabout
Joel Lexchin

Bibliographic record

VenueInternational Journal of Risk & Safety in Medicine · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsYork University
Fundersnot available
KeywordsGovernment (linguistics)BusinessDrug CompanyDescriptive statisticsDrugPosition (finance)Brand namesProduct (mathematics)Drug withdrawalMedicineMarketingFinancePsychiatryManagementEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Companies often defending their products when there are concerns about their safety and/or effectiveness. OBJECTIVE: This study looks at drugs removed from the Canadian market from 1990 onward and examines how companies responded. METHODS: This descriptive analysis used a previously published article and a hand search of a Government of Canada website to generate a list of drugs withdrawn from Canada from 1990 onwards. For each product the following information was extracted: brand name, generic name, company, date of withdrawal and evidence base for withdrawal. Google and Factiva searches were used to identify sources containing statements from the company about the withdrawal. Statements were independently graded by two people into the following categories: company agrees with the withdrawal; drug could be used safely with certain precautions; company may reintroduce the drug; company disagrees with the withdrawal. Searches were carried out between September 15-20, 2020. RESULTS: There were 22 drugs for which there were company statements. In 10 statements, the companies disagreed with the decision to withdraw the drug and in 7 they agreed with the decision. In the other 5 cases they felt that the drug could have been kept on the market with restrictions (2 cases) or they might reintroduce the drug (3 cases). The level of evidence for the withdrawal did not seem to influence the companies' position. CONCLUSION: In 15 out of 22 cases, the company either disagreed with the decision to withdraw the drug or felt that the drug should continue to be available to Canadian 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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.037
GPT teacher head0.327
Teacher spread0.290 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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