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Record W4408251139 · doi:10.1016/j.eats.2025.103497

Arthroscopic Posterior Glenoid Augmentation With a Fresh Distal Tibia Allograft

2025· article· en· W4408251139 on OpenAlexaff
Mikalyn T. DeFoor, Emily Whicker, Marco Adriani, Ryan J. Whalen, Nate J Dickinson, Broderick T Provencher, Matthew T. Provencher

Bibliographic record

VenueArthroscopy Techniques · 2025
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsSurgical Specialties (Canada)
FundersAmerican Shoulder and Elbow SurgeonsAmerican Academy of Orthopaedic SurgeonsArthroscopy Association of North AmericaAmerican Orthopaedic Society for Sports MedicineArthrex
KeywordsMedicinePosterior shoulderSurgeryArticular surfaceTibiaDistal tibia

Abstract

fetched live from OpenAlex

Recurrent posterior instability, resulting in posterior glenoid bone loss, or significant posterior glenoid bone loss at the index procedure can be a cause of failure of arthroscopic posterior stabilization repair. There are several described autograft and allograft options to restore posterior glenoid bone stock if the posterior glenoid bone loss is significant, generally greater than 20% of the surface of the glenoid. Advantages of a fresh distal tibia allograft include a contour near anatomic to the native glenoid with an articular surface that matches the humeral head through arc of motion and no associated donor site morbidity. This technique article outlines an approach to a failed arthroscopic posterior capsulolabral repair with an arthroscopic fresh distal tibia osteochondral allograft for recurrent posterior shoulder instability in the setting of glenoid bone loss.

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.324
Teacher spread0.316 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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