Latarjet as a primary and revision procedure for anterior shoulder instability – A comparative study of survivorship, complications and functional outcomes in the medium to long-term
Bibliographic record
Abstract
BACKGROUND: This retrospective study aims to compare the outcome of the Latarjet procedure when used as a primary or revision procedure for recurrent anterior gleno-humeral instability. METHODS: One hundred and ninety-seven patients underwent 205 open Latarjet procedures during the period 2006-2015 (mean follow-up 5.6 years). Sixty shoulders had failure of a previous stabilisation requiring revision to the Latarjet procedure. Outcomes were measured using the Western Ontario Shoulder Instability Index and Quick Disabilities of the Arm, Shoulder and Hand score. Survival analyses were performed using Kaplan-Meier curves, and multiple linear regression modelling was utilised to identify predictors of functional outcome (p < 0.05). RESULTS: Two shoulders had recurrent dislocations in the cohort of 205 (1.0%). Six shoulders underwent further surgery for non-instability complications (2.9%). There were no significant differences in the clinical or functional outcome between patients undergoing a primary Latarjet procedure and those who required revision of a failed soft-tissue stabilisation. Ninety-two per cent of patients were satisfied with their shoulder following surgery. Patient-reported instability and satisfaction was significantly associated with poorer functional scores. DISCUSSION: The Latarjet procedure successfully prevents recurrent anterior instability and is associated with high levels of satisfaction. Patient-reported outcome measures suggest no difference between primary and revision procedures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".