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Potential bioactive coating system for high-performance absorbable magnesium bone implants

2021· review· en· W3209529515 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

VenueBioactive Materials · 2021
Typereview
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaUniversiti Kebangsaan Malaysia
KeywordsMagnesiumCorrosionMaterials scienceCoatingBone healingMetallurgyBiomedical engineeringComposite materialSurgery

Abstract

fetched live from OpenAlex

Magnesium alloys are considered the most suitable absorbable metals for bone fracture fixation implants. The main challenge in absorbable magnesium alloys is their high corrosion/degradation rate that needs to be controlled. Various coatings have been applied to magnesium alloys to slow down their corrosion rates to match their corrosion rate to the regeneration rate of the bone fracture. In this review, a bioactive coating is proposed to slow down the corrosion rate of magnesium alloys and accelerate the bone fracture healing process. The main aim of the bioactive coatings is to enhance the direct attachment of living tissues and thereby facilitate osteoconduction. Hydroxyapatite, collagen type I, recombinant human bone morphogenetic proteins 2, simvastatin, zoledronate, and strontium are six bioactive agents that show high potential for developing a bioactive coating system for high-performance absorbable magnesium bone implants. In addition to coating, the substrate itself can be made bioactive by alloying magnesium with calcium, zinc, copper, and manganese that were found to promote bone regeneration.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.036
GPT teacher head0.281
Teacher spread0.246 · 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