Evaluation of the Efficacy and Performance of Medical Implants: A Review
Bibliographic record
Abstract
An implant can be defined, in a medical context, as biological or artificial materials inserted or grafted into the body. Implants may be sensory devices (cochlear, ocular), mechanical devices that are 'passive' (orthopedic joint replacements and fixation plates, dental implants, coronary artery stents and vascular grafts) or 'active' (left ventricular assist devices, heart valves) electrophysiological stimulation devices (cardiac or gastric pacemakers, implantable cardiac defibrillators, functional electrical stimulators for epilepsy or Parkinson's disease) or medication administration devices (insulin or analgesic delivery pumps) or intra-ocular sustained drug release implants. Implantation has had a long history in several subspecialties of medicine. Evaluation of the efficacy of implants is a multifactorial issue. Several variables need to be considered while studying the rejection of the implants such as pathophysiological mechanisms, malfunction, design shortcomings and improper implementation/implantation by a medical team. This paper identifies a variety of modes of failure and how they affect the overall efficacy of the device technologies. Suggestions for improvement, as outlined in the literature, will be examined.
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 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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".