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Record W2032710553 · doi:10.1089/ten.teb.2010.0572

Mechanical Properties of Natural Cartilage and Tissue-Engineered Constructs

2011· review· en· W2032710553 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

VenueTissue Engineering Part B Reviews · 2011
Typereview
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCartilageScaffoldTissue engineeringMaterials scienceBiomedical engineeringEngineeringAnatomyMedicine

Abstract

fetched live from OpenAlex

There has been much research over the past two decades with the aim of engineering cartilage constructs for repairing or restoring damaged cartilage. To engineer healthy neocartilage, the constructs must have mechanical properties matching those of native cartilage as well as appropriate for the loading conditions of the joint. This article discusses the mechanical behavior of native cartilage and surveys different types of tensile, compressive, and shear tests with their limitations. It also comprehensively reviews recent work and achievements in developing the mathematical models representing the mechanical properties of both native and engineered cartilage. Different methods for enhancing the mechanical properties of engineered cartilage are also discussed, including scaffold design, mechanical stimulation, and chemical stimulation. This article concludes with recommendations for future research aimed at achieving engineered cartilage with mechanical properties matching those found in native cartilage.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.000
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 head0.062
GPT teacher head0.290
Teacher spread0.228 · 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