Cardiovascular Medical Device Failure: Using Five-Week Moving Averages To Assess Adverse Event Report Data
Bibliographic record
Abstract
The COVID-19 pandemic had a variety of effects on the healthcare system, including the interruption of regular cardiology practices. We examined the pandemic’s effects on cardiovascular medical device failure by investigating trends in the number of reports of adverse events of several cardiovascular medical devices over the span of three years, including the first year of the pandemic. Specifically, we used data from the FDA’s MAUDE database, calculating the five-week moving average of adverse events associated with both implantable cardioverter defibrillators and coronary drug-eluting stents. We previously reported a 46% decrease in reported deaths attributed to ICDs and a 27% decrease in reported injuries attributed to coronary DES. We use a five-week moving average and confirm a 46% decrease in reported deaths attributed to ICDs, report a 9.8% increase in ICD-attributed malfunctions, and confirm a 27% decrease in reported injuries attributed to coronary DES. The different effects of the pandemic on these adverse event report trends, even within one device, show there are more factors to consider than explanations such as underreporting which would be expected to affect most medical devices relatively homogeneously.
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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 | 0.067 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".