Updates on the research and development of absorbable metals for biomedical applications
Bibliographic record
Abstract
Absorbable metals, metals that corrode in physiological environment, constitute a new class of biomaterials intended for temporary medical implant applications. The introduction of these metals has shifted the established paradigm of metal implants from preventing corrosion to its direct application. Interest toward absorbable metals has been growing in the past decade. This is proved by the rapid increase in scientific publication, progressive development of standards, and launching the first commercial products. Iron, magnesium, zinc, and their alloys are the current three absorbable metals families. Magnesium-based metals are the most progressing family with a large data set obtained from both basic and translational research. Iron-based metals are still facing a major challenge of low in vivo corrosion rate despite the significant efforts that have been put to overcome its weakness. Zinc-based metals are the new alternative absorbable metals with moderate corrosion rates that fall between those of iron and magnesium. This manuscript provides a brief review on the latest progress in the research and development of absorbable metals, the most important findings, the remaining challenges, and the perspective on the future direction.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".