MétaCan
Menu
Back to cohort
Record W1831360785 · doi:10.1039/c5bm00163c

Composite biopolymers for bone regeneration enhancement in bony defects

2015· review· en· W1831360785 on OpenAlexafffund
Kaushar Jahan, Maryam Tabrizian

Bibliographic record

VenueBiomaterials Science · 2015
Typereview
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsBiocompositeBiomaterialScaffoldBiocompatibilityRegeneration (biology)Natural polymersBiomedical engineeringPolymerMaterials scienceBiocompatible materialNatural materialsComposite numberNanotechnologyPolymer scienceComposite materialMedicineBiology

Abstract

fetched live from OpenAlex

For the past century, various biomaterials have been used in the treatment of bone defects and fractures. Their role as potential substitutes for human bone grafts increases as donors become scarce. Metals, ceramics and polymers are all materials that confer different advantages to bone scaffold development. For instance, biocompatibility is a highly desirable property for which naturally-derived polymers are renowned. While generally applied separately, the use of biomaterials, in particular natural polymers, is likely to change, as biomaterial research moves towards mixing different types of materials in order to maximize their individual strengths. This review focuses on osteoconductive biocomposite scaffolds which are constructed around natural polymers and their performance at the in vitro/in vivo stages and in clinical trials.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.044
GPT teacher head0.309
Teacher spread0.266 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations102
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueBiomaterials ScienceSame topicBone Tissue Engineering MaterialsFrench-language works237,207