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Record W4364375163 · doi:10.1002/pat.6046

Synthetic polymers as bone engineering scaffold

2023· article· en· W4364375163 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

VenuePolymers for Advanced Technologies · 2023
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsScaffoldMaterials scienceBiocompatibilityTissue engineeringBiomedical engineeringBone tissueRegeneration (biology)Hard tissueBiomaterialSoft tissuePolymerAdaptabilityNanotechnologyComposite materialDentistryEngineeringSurgeryMedicine

Abstract

fetched live from OpenAlex

Abstract Damage or loss of the bone tissue leads to mobility decline contributing to one of the major issues of human well‐being. Tissue engineering is used to recover fractures and damaged parts of the bone tissue. Various biomaterials and scaffolds have been evaluated for regeneration of hard tissues, however, polymers are the most frequently required biomaterials for the improvement of synthetic bone scaffolds due to their suitable mechanical properties and similar degradation rates to the proteins in hard and soft tissues. Synthetic polymeric materials in bone replacement have several advantages because their physical characteristics can be designed according to their application and their composition can be changed easily. On the other hand, the high adaptability, tenability, and biocompatibility of synthetic materials have received considerable acceptance in the field of tissue engineering. This article presents an update on materials for the fabrication of scaffolds in bone tissue engineering; in addition, we provide information of different kinds of scaffolds and their application usage for hard tissue 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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