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Record W2989676473 · doi:10.1016/j.eats.2019.08.004

Posterior Glenohumeral Capsular Reconstruction With Modified McLaughlin for Chronic Locked Posterior Dislocation

2019· article· en· W2989676473 on OpenAlexaff
Graeme Matthewson, Ivan Wong

Bibliographic record

VenueArthroscopy Techniques · 2019
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of ManitobaDalhousie UniversityPan Am Clinic
Fundersnot available
KeywordsMedicineSurgeryDislocation

Abstract

fetched live from OpenAlex

Posterior instability is relatively rare when compared with anterior instability but can comprise up to 40% of operatively treated instability cases. Posterior dislocations are much rarer and are classically due to trauma, seizure, or electric shock. Due to a lack of an obvious deformity and an internally rotated and adducted arm position, posterior shoulder dislocations often are missed on initial presentation. In the management of posterior dislocations, considerations need to be made in regard to bony and soft-tissue pathology. In the setting of soft-tissue deficiency, previous options included nonoperative management primarily consisting of bracing and activity modification as well as arthroplasty options that do not rely on the capsulolabral complex for stability. In this paper, we present a technique for treating a chronic posterior shoulder dislocation with an associated large reverse Hill-Sachs deformity. In this setting, a revision labral repair and capsulodesis is generally not possible due to insufficient capsulolabral tissues. Here, we present the technique for an arthroscopic posterior capsule reconstruction using an acellular dermal allograft as well as a McLaughlin procedure for the treatment of a reverse Hill-Sachs lesion.

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 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 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 score0.164
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.013
GPT teacher head0.299
Teacher spread0.286 · 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 teacher head, 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

Citations7
Published2019
Admission routes1
Has abstractyes

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