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Record W3130213199 · doi:10.1002/mabi.202000365

Biomaterial Properties Modulating Bone Regeneration

2021· review· en· W3130213199 on OpenAlexaff
Yi Zhu, Cynthia Goh, Annie Shrestha

Bibliographic record

VenueMacromolecular Bioscience · 2021
Typereview
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsCanada Research ChairsUniversity of Toronto
Fundersnot available
KeywordsBiomaterialRegeneration (biology)Tissue engineeringChemistryBone healingScaffoldBiomedical engineeringCell biologyMaterials scienceNanotechnologyBiologyMedicineAnatomy

Abstract

fetched live from OpenAlex

Biomaterial scaffolds have been gaining momentum in the past several decades for their potential applications in the area of tissue engineering. They function as three-dimensional porous constructs to temporarily support the attachment of cells, subsequently influencing cell behaviors such as proliferation and differentiation to repair or regenerate defective tissues. In addition, scaffolds can also serve as delivery vehicles to achieve sustained release of encapsulated growth factors or therapeutic agents to further modulate the regeneration process. Given the limitations of current bone grafts used clinically in bone repair, alternatives such as biomaterial scaffolds have emerged as potential bone graft substitutes. This review summarizes how physicochemical properties of biomaterial scaffolds can influence cell behavior and its downstream effect, particularly in its application to 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.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.033
GPT teacher head0.254
Teacher spread0.221 · 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

Citations76
Published2021
Admission routes1
Has abstractyes

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