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Record W2767508767 · doi:10.21037/aoj.2017.10.09

Assessment of bone loss in anterior shoulder instability

2017· article· en· W2767508767 on OpenAlex
Cory A. Kwong, Eva M. Gusnowski, Kelvin K. W. Tam, Ian K.Y. Lo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnals of Joint · 2017
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAnterior shoulderMedicineBankart lesionSoft tissueSurgeryOrthodonticsLesion

Abstract

fetched live from OpenAlex

Anterior shoulder dislocations commonly result in predictable patterns of osseous injury on both the glenoid and humeral side. The presence of bone loss contributes to the risk of recurrent dislocations, as well as the success of surgical intervention. For example, in patients with glenoid lesions comprising >25% of the glenoid surface or Hill-Sachs lesions that “engage” the glenoid rim, the recurrence rate has been reported to be as high as 67% after soft tissue Bankart repair. The range of injury severity and anatomic variations in soft tissue and bony injury patterns associated with anterior shoulder instability makes identification and quantification of these lesions critical prior to surgical intervention. Historically, bony lesions on the glenoid and humeral side were considered independently. More recently, the interaction between the two and their summative effects on recurrence and operative outcomes has become better understood. The purpose of this review is to provide an overview of the historical methods of identifying bony lesions, as well as an update on current concepts in quantifying bone loss in anterior shoulder instability.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.138
GPT teacher head0.441
Teacher spread0.303 · 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