Quality of medicines in Canada: a retrospective review of risk communication documents (2005–2013)
Bibliographic record
Abstract
OBJECTIVE: To explore the quality and safety of medicines in Canada. DESIGN: A retrospective review of drug recalls and risk communication documents conveying issues relating to defective (ie, substandard and falsified) medicines. SETTING: The Health Canada website search for drug recalls and risk communication documents issued between 2005 and 2013. ELIGIBILITY CRITERIA: Drug recalls and risk communication documents related to quality defect in medicinal products. MAIN OUTCOME MEASURE: Relevant data about defective medicines reported in drug recalls and risk communication documents, including description of the defect, type of formulation, year of the recall and category of the recall or the document. RESULTS: There were 653 defective medicines of which 649 were substandard. The number of defective medicines reported by Health Canada increased from 42 in 2005 to 143 in 2013. The two most frequently reported types of defects were stability (205 incidents) and contamination issues (139 incidents). Some of these defects were found to be more prominent and repetitive over other types within some manufacturers. Tablet formulation (251 incidents) was the formulation most frequently compromised. No significant differences were observed between the manufacturers and distributors in the number of substandard medicines reported under each defect type. There were only four falsified medicines reported over the 9-year period. CONCLUSIONS: Substandard medicines are a problem in Canada and have resulted in an increasing number of recalled medicines. Most of the failures were related to stability issues, raising the need to investigate the root causes and for stringent preventative measures to be implemented by manufacturers.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.031 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".