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Biomaterial scaffolds in maxillofacial bone tissue engineering: A review of recent advances

2023· review· en· W4388581435 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.

Bibliographic record

VenueBioactive Materials · 2023
Typereview
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsBiomaterialRegeneration (biology)ScaffoldTissue engineeringBiomedical engineeringDentistryMedicineBiology

Abstract

fetched live from OpenAlex

Maxillofacial bone defects caused by congenital malformations, trauma, tumors, and inflammation can severely affect functions and aesthetics of maxillofacial region. Despite certain successful clinical applications of biomaterial scaffolds, ideal bone regeneration remains a challenge in maxillofacial region due to its irregular shape, complex structure, and unique biological functions. Scaffolds that address multiple needs of maxillofacial bone regeneration are under development to optimize bone regeneration capacity, costs, operational convenience. etc. In this review, we first highlight the special considerations of bone regeneration in maxillofacial region and provide an overview of the biomaterial scaffolds for maxillofacial bone regeneration under clinical examination and their efficacy, which provide basis and directions for future scaffold design. Latest advances of these scaffolds are then discussed, as well as future perspectives and challenges. Deepening our understanding of these scaffolds will help foster better innovations to improve the outcome of maxillofacial bone tissue engineering.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.866
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.043
GPT teacher head0.315
Teacher spread0.273 · 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