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Record W2109814359 · doi:10.2106/jbjs.j.01983

Arthroscopic Bankart Repair and Capsular Shift for Recurrent Anterior Shoulder Instability

2012· article· en· W2109814359 on OpenAlexaboutno aff
Issaq Ahmed, Fiona Ashton, C. M. Robinson

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBankart repairAnterior shoulderSurgeryBankart lesionArthroscopyRetrospective cohort studyDashComplicationProportional hazards model

Abstract

fetched live from OpenAlex

BACKGROUND: Arthroscopic Bankart repair and capsular shift is a well-established technique for the treatment of anterior shoulder instability. The purpose of this study was to evaluate the outcomes following arthroscopic Bankart repair and capsular shift and to identify risk factors that are predictive of recurrence of glenohumeral instability. METHODS: We performed a retrospective review of a prospectively collected database consisting of 302 patients who had undergone arthroscopic Bankart repair and capsular shift for the treatment of recurrent anterior glenohumeral instability. The prevalence of patient and injury-related risk factors for recurrence was assessed. Cox proportional hazards models were used to estimate the predicted probability of recurrence within two years. The chief outcome measures were the risk of recurrence and the two-year functional outcomes assessed with the Western Ontario shoulder instability index (WOSI) and disabilities of the arm, shoulder and hand (DASH) scores. RESULTS: The rate of recurrent glenohumeral instability after arthroscopic Bankart repair and capsular shift was 13.2%. The median time to recurrence was twelve months, and this complication developed within one year in 55% of these patients. The risk of recurrence was independently predicted by the patient's age at surgery, the severity of glenoid bone loss, and the presence of an engaging Hill-Sachs lesion (all p < 0.001). These variables were incorporated into a model to provide an estimate of the risk of recurrence after surgery. Varying the cutoff level for the predicted probability of recurrence in the model from 50% to lower values increased the sensitivity of the model to detect recurrences but decreased the positive predictive value of the model to correctly predict failed repairs. There was a significant improvement in the mean WOSI and DASH scores at two years postoperatively (both p < 0.001), but the mean scores in the group with recurrence were significantly lower than those in the group without recurrence (both p < 0.001). CONCLUSIONS: Our study identified factors that are independently associated with a higher risk of recurrence following arthroscopic Bankart repair and capsular shift. These data can be useful for counseling patients undergoing this procedure for the treatment of recurrent glenohumeral instability and individualizing treatment options for particular groups of patients. LEVEL OF EVIDENCE: Prognostic level I. See Instructions for authors for a complete description of levels of evidence.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.313
Teacher spread0.262 · 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 designObservational
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

Citations184
Published2012
Admission routes1
Has abstractyes

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