Graduated Introduction of Orthopaedic Implants: Encouraging Innovation and Minimizing Harm
Bibliographic record
Abstract
There is continued pressure for the development of innovative orthopaedic surgical devices and techniques to meet the demands of increasingly younger and more active patients. However, as demonstrated by several recent orthopaedic implant withdrawals and recalls, clinically important unknown modes of failure for newly introduced devices may not become apparent for several years after widespread adoption, affecting a large number of patients. Different reasons have been implicated for this problem, including weaknesses in the United States medical device approval process, as well as deficiencies in mechanisms for post approval implant performance monitoring. Several remedies have been proposed over the past decades. We aim to stimulate discussion concerning the adoption of orthopaedic technology by describing the concept of a graduated implant approval process for orthopaedic devices that builds on recommendations previously made by other authors; by explaining how this will benefit patients, surgeons, and device manufacturers; and by clarifying why the time has come for the orthopaedic community to reconsider the adoption of such a process.
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 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".