Do medical device monopolies impact safety and quality? Analysis of adverse events for Abiomed Impella(R) heart pumps
Bibliographic record
Abstract
Monopolistic market conditions eliminate competition and reduce incentives for product improvement.This research investigates the safety and performance of a life-saving cardiovascular medical device when one manufacturer monopolises the market.The Abiomed Impella(R) heart pump provides hemodynamic support for the heart.As there are no other manufacturers of similar devices, Abiomed maintains a monopoly on these medical devices.The Food and Drug Administration (FDA) Manufacturer and Use Facility Device Experience (MAUDE) database lists adverse events for medical devices, and provides insights into adverse event types and occurrences.This study analysed adverse events, including deaths, injuries, and malfunctions, for Abiomed Impella(R) devices from 2008 to 2021.The data reveal a striking increase in reported malfunctions over the decade from 2011 to 2021, during which annual malfunctions rose nine-fold from 10 to 90.Of the 285 total malfunctions reported for Abiomed Impella(R) devices in the decade 2011 to 2021, almost one-third of malfunctions occurred in 2021.Reported injuries for Abiomed Impella(R) devices rose even more dramatically, from one annual injury to 374 annual injuries between 2008 and 2021.Reported deaths for Abiomed Impella(R) devices also rose from 2 annual deaths to 17 annual deaths between 2012,and 2021.Most concerning, many patient deaths were misreported as injuries or malfunctions.Only 33% of deaths associated with Abiomed Impella(R) devices were accurately reported as deaths.These results highlight the need for continuous improvement and transparent reporting for medical devices; it can mean the difference between life and death.
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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".