Quality of Evidence of Recalled Medical Devices Associated with Serious Health Risks: A Scoping Review
Bibliographic record
Abstract
Many high-risk implanted medical devices have been subject to recalls. The HeartMate II, a left ventricular assist device subject to a recall in 2014, was evaluated by systematically searching the literature on the device to see if a physician could have anticipated the problems that led to the recall. Eighty studies were randomly selected, as well as 20 published in the highest impact factor journals. 63.8% and 65% of the studies in each analysis, respectively, reported positive outcomes. However, the study designs were subject to bias, sample sizes were small, and many reported conflicts of interest. The overall quality of the evidence was low, and it would have been hard for a physician to have anticipated device problems as few of the studies reported on outcomes related to the recall. Further research is required to ensure greater security to the patients who depend on these advancements in medical technology.
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 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 | 0.041 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".