Investigation of Reported Adverse Events for Bioresorbable Coronary Artery Stents
Bibliographic record
Abstract
With an estimated number of 610,000 annual deaths in the United States alone, Coronary Artery Disease (CAD) is among the leading causes of death worldwide.Severe cases of CAD are traditionally treated through coronary artery bypass grafting, a surgical procedure highly invasive in nature.Stenting has subsequently gained popularity as a minimally invasive alternative to treat milder cases of CAD.However, bare-metal stents often cause complications such as restenosis and jailing of arterioles, and drug-eluting stents aimed at mitigating restenosis did not solve the arteriolar jailing.Thus in 2016, the FDA approved the Abbott Absorb Bioresorbable Vascular Scaffold (BVS) System, a bioresorbable drug-eluting stent designed to provide temporary arterial support and dissolve, thereby avoiding permanent metal implants.Despite theoretical advantages of this design, clinical adoption has been limited.This study investigates the reasons behind the limited use of bioresorbable drug-eluting stents by analyzing reported adverse event data (injuries, malfunctions, deaths) from the Manufacturer and User Facility Device Experience (MAUDE) database.The data showed a proportionally larger number of injuries associated with bioresorbable stents in comparison to bare-metal stents, a statistic driven primarily by major cardiac events.The ABSORB III pivotal clinical study found higher adverse event rates associated with the BVS system when compared to bare-metal stents; these findings resulted in the 2017 FDA implementation of a Class I recall for the Absorb BVS stent.The COVID-19 pandemic may have also contributed to the observed spike in reported adverse events in 2020 as a product of increased healthcare challenges and overall worse patient health.The results of this study highlight a need for improved treatment options or stent designs to enhance CAD patient outcomes.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".