MEDICAL DEVICE RECALLS IN CANADA FROM 2005 TO 2015
Bibliographic record
Abstract
OBJECTIVES: Medical devices are ubiquitous in modern medical care. However, little is known about the epidemiology of medical devices in the healthcare marketplace, including the rate at which medical devices are subject to recalls or other advisories. We sought to study the epidemiology of medical devices in Canada, focusing on device recalls. In Canada, a recall may signify a variety of events, ranging from relatively minor field safety notifications, to removal of a product from the marketplace. METHODS: We used data from Health Canada to study medical device recalls in Canada from 2005 to 2015. We analyzed the risks of medical device recalls according to the risk class of the device (I lowest; IV highest) and the hazard priority of the recall (Type I highest potential harm; Type III lowest potential harm). RESULTS: During a 10-year period, there were 7,226 medical device recalls. Most recalls were for intermediate risk class (Class II, 40.1 percent; Class III, 38.7 percent) medical devices. Among recalled devices, 5.0 percent were judged to have a reasonable probability of serious adverse health consequences or death (Type I recall Hazard Priority classification). While the number of medical devices marketed in Canada is not known, over a similar 10-year period, 24,849 new Class II, II, and IV medical device licenses were issued by Health Canada. CONCLUSIONS: Several hundred medical device recalls occur in Canada each year. Further research is needed to characterize the nature of medical device recalls, and to explore how consumers use information about recalls.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".