Companies’ statements about drugs withdrawn from the Canadian market: A descriptive analysis
Bibliographic record
Abstract
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used a previously published article and a hand search of a Government of Canada website to generate a list of drugs withdrawn from Canada from 1990 onwards.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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".