Preface to Special Section: Retrieval Analysis of Implanted Medical Devices
Bibliographic record
Abstract
Retrieval Analysis of Implanted Medical DevicesEach year millions of patients improve their quality of life through surgical procedures that involve implanted medical devices.As the rising cost of health care continues to be debated within our country, it is clear that we must be certain that Americans are receiving the best, most cost eff ective health care treatments.Advances in medical technology continue to be a large part of the rising cost of health care, and the decision to provide the increased cost of care due to new technology must be determined from evidence-based studies.Th e Food and Drug Administration this past year issued an internal report where they stated that science-based reviews must be strived for and that the agency must fi rst provide safe devices for patients while making sure not to impede the advancement and innovation of technology in the medical fi eld.With the focus on both cost and innovation, it is clear that everything must be done to make certain that the devices we utilize in medicine are safe and that they are given a pathway to be improved on using science and lessons learned from device retrieval studies.Retrieval analysis studies of medical devices have been utilized for decades to learn lessons from failed devices removed at the time of revision or repeated procedures, as well as from those that functioned well for the life of a patient and were obtained at necropsy.Th e value of implant retrieval analysis in orthopaedic surgery has been well recognized in advancing implant longevity.If we use the example of total hip and knee arthroplasty, we know that analysis of devices has provided insight into wear, corrosion, design characteristics, and material issues that have advanced the design and longevity of these devices.Prosthetic implants retrieved at revision surgery (for implant failure) or devices retrieved postmortem from patients with clinically successful reconstructions provide a unique set of specimens that can be studied to evaluate the eff ect of the implant on the host environment and vice versa.In this issue, there are two articles that try to determine commonalities of patellar polyethylene buttons from total knee arthroplasty, with one obtained from necropsy retrievals and the other from implants obtained at time of revision surgery.A systematic analysis of retrieved components, in combination with histologic, radiographic, and clinical data, can provide valuable insights into the mechanisms of failure of the biomaterials and implant designs used in joint replacement applications.If computer modeling can be added to properly predict wear patterns and kinematics of the joint, then the fi rst steps toward intraoperative computer assistance for optimal functionality and implant longevity may be realized.Th is type of analysis can be seen in this issue's article that utilizes a 3D dynamic TKA model to show how the kinematics and contact stresses change after wear of the polyethylene insert (Williams et al.).Th e fi rst retrieval observations were performed for failures of implants during their subsequent revision surgeries.While these studies gave valuable insight into the failure modes of early designs and biomaterials, it was not until the 1980s when donor-related studies were organized.At that time, usually the establishment of a retrieval program within a practice of orthopaedic surgeons allowed for the analysis of a series of patients who had a specifi c implant utilized at the time of surgery.Th ere are few retrieval programs that encompass a large variety of implants, and many times those laboratories only receive the implant itself without the surrounding joint and musculoskeletal tissues.It has been evident from past
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.043 | 0.034 |
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".