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Record W4238406875 · doi:10.1533/abib.2004.1.2.115

Torn ACL: a new bioengineered substitute brought from the laboratory to the knee joint

2004· article· en· W4238406875 on OpenAlexfundno aff
Francine Goulet, Denis Rancourt, Réjean Cloutier, Pierrot Tremblay, Anne-Marie Belzil, Jean Lamontagne, Marc Bouchard, Julie Tremblay, Louis‐Mathieu Stevens, Julie Labrosse, Ève Langelier, Marc D. McKee

Bibliographic record

VenueApplied Bionics and Biomechanics · 2004
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
FundersSmith and NephewUniversité Laval
KeywordsFibrocartilageAnterior cruciate ligamentKnee JointFemurTibiaCollagen fibrilLigamentMedicineSurgeryAnatomyOsteoarthritisPathologyArticular cartilage

Abstract

fetched live from OpenAlex

Anterior cruciate ligament (ACL) injuries occur at an annual rate of 120 000 in the USA, and many need reconstructive surgery. We report successful results at 1–13 months following implantation of bioengineered ACL (bACL) in goats. A bACL has been developed using autologous ACL cells, a collagen matrix and bone plugs. The extremities of the bACL were fully integrated into the femur and tibia of the host. Vascularisation of the grafts was extensive 1 month post-surgery and improved with time. At 6 months postgrafting, histological and ultrastructural observations demonstrated a highly organised ligamentous structure, rich in type I collagen fibres and fibroblasts. At the implants’ insertion sites, characteristic fibrocartilage was observed having well aligned chondrocytes and collagen fibrils. After a year, mechanical rupture of the grafts demonstrated a major gain in strength. Eventual applications of this new technology in humans include multiple uses in orthopaedic, dental and reconstructive surgeries.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations8
Published2004
Admission routes1
Has abstractyes

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