Special Issue: Evaluation of the Performance of ImplantsGuest Editors: Markad V. Kamath & Adrian R. UptonPreface: Evaluation of the Performance of Implants
Bibliographic record
Abstract
Implantation technology has reduced our pain, as well as made us live longer and have healthier lives, through engineering design and production of replacement parts, or through other novel procedures such as modification of natural grafts or tissue engineering and regenerative medicine using stem cell engineering. While pacemakers, heart valves, and bone implants have been recognized as viable therapies for some time, implants for cochlear malfunction, intraocular therapy, and left ventricular assist devices have achieved critical mass only recently for them to be claimed as mainstream therapy. Additional paradigms that are now a part of the physician's tool kit include devices for targeted drug delivery, regenerative therapy using stem cells, and continuous monitoring of the body's internal physiological variables, just to name a few. In this context, the role of engineers in better designing and developing novel and viable devices places them under greater scrutiny. Also, there is a need to study and document limitations and deficiencies of all existing implants so as to improve their performance and efficacy with the next generation of implantable devices. Performance metrics have to be adjusted upward regularly based on experience.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".