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Record W2803136671 · doi:10.1007/s40204-018-0091-4

Updates on the research and development of absorbable metals for biomedical applications

2018· review· en· W2803136671 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProgress in Biomaterials · 2018
Typereview
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCorrosionZincMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.246
GPT teacher head0.438
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it