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Record W2808935381 · doi:10.1016/j.eats.2018.03.011

Posterior Glenohumeral Capsular Reconstruction Using an Acellular Dermal Allograft

2018· article· en· W2808935381 on OpenAlexaff
Jillian Karpyshyn, Erin Gordey, Catherine Coady, Ivan Wong

Bibliographic record

VenueArthroscopy Techniques · 2018
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsQueen Elizabeth II Health Sciences CentreNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsMedicinePosterior shoulderSurgeryConservative managementLigamentous laxity

Abstract

fetched live from OpenAlex

Posterior shoulder instability is an uncommon and challenging cause of shoulder pain and dysfunction. Surgical management has less reliable results and higher failure rates compared with techniques for anterior shoulder instability. The presence of generalized ligamentous laxity further complicates options for surgical management. If primary capsulolabral repair fails, controversy exists as to the optimal revision procedure. This technical description and video present an arthroscopic technique for reconstruction of the posterior glenohumeral capsule with an acellular dermal allograft to treat posterior instability in a patient with Ehlers-Danlos syndrome and a previously failed posterior capsular plication.

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 head0.036
GPT teacher head0.354
Teacher spread0.318 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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