Improving the Quality of Life of Patients With Medical Devices by a Timely Analysis of Adverse Events
Bibliographic record
Abstract
Implanted and non-implanted medical devices, including artificial joints, are widely accepted to improve patients' quality of life (QOL). While implant survival rates of over 80% can be accepted for artificial joints, there is still a need to achieve higher survival rates at 15 years or longer to reduce the need for revisions due to implant failure before the end of the patient's life. Therefore, artificial joints are constantly improved with design changes and new designs, including modified or new materials. Most of these improvements perform as expected, but cases still exist where unknown failures occur, requiring premature revisions. Examples of such unsuccessful improvements in the last twenty years are mentioned in this technical case report. The main focus of this paper is on an acetabular cup that was recalled due to unexpected revisions after a few weeks to a few months in vivo. The main reason for the revisions were small amounts of an oily residue containing endotoxins trapped inside the porous coating applied to the cup to facilitate bone ingrowth. The cup was recalled within four months after the company become aware of the problem, and prior to knowing exactly why the cups were failing early. The root cause analysis (RCA) took several more months to complete. The lessons learned during the analysis are discussed so that similar events in other implantable medical devices can be avoided. The acetabular cup case aims to highlight that a timely RCA, triggered by few unexplained revisions, will benefit patients and improve QOL.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".