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Record W2616988746 · doi:10.1016/j.eats.2017.01.005

Labral Gracilis Tendon Allograft Reconstruction and Cartilage Regeneration Scaffold for an Uncontained Acetabular Cartilage Defect of the Hip

2017· article· en· W2616988746 on OpenAlexaffabout
Laurel E. MacInnis, Ahmed Al Hussain, Catherine Coady, Ivan Wong

Bibliographic record

VenueArthroscopy Techniques · 2017
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsNova Scotia Health AuthorityDalhousie UniversityMarkham Stouffville Hospital
Fundersnot available
KeywordsMedicineCartilageLabrumAcetabular labrumAcetabulumSurgeryAnatomyTendonArticular cartilageFemoral headHip painRegeneration (biology)ArthroscopyOsteoarthritisPathologyBiology

Abstract

fetched live from OpenAlex

Hip cartilage injuries are very common, with rates as high as 50% having been reported in some series; abnormal femoral acetabular contact can result in a full-thickness cartilage defect or labral lesion. The prevalence of labral lesions can be as high as 55%. This Technical Note describes an arthroscopic technique to reconstruct an uncontained, full-thickness, focal cartilage defect of the acetabulum, with reconstruction of the missing labrum using a gracilis allograft and use of a biological liquid scaffold for cartilage reconstruction. Capsulotomy, acetabuloplasty, and microfracture with marrow bleeding should be performed simultaneously with the gracilis allograft preparation. The graft is inserted and anchored to reconstruct the missing labrum and to re-create a contained defect. Suction and drying of the joint surfaces are performed while the mixture of BST-CarGel (Piramal Healthcare, Laval, Quebec, Canada) and blood is prepared. A drop-by-drop technique is then used to reconstruct the cartilage defect.

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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.305
Teacher spread0.284 · 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 designCase report
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
Published2017
Admission routes2
Has abstractyes

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