Does high body mass index increase the risk of shoulder instability surgery? The LUXE prospective cohort study on 227 recurrent anterior shoulder instability
Bibliographic record
Abstract
Background The aim of this study was to determine whether body mass index (BMI) plays a role in overall morbidity following shoulder instability surgery and whether some surgical techniques are BMI-sensitive. Methods A prospective, multicenter database was created that included the following three surgical techniques: arthroscopic Bankart (AB), arthroscopic Bankart with remplissage (ABR), and Open Latarjet (OL). Patient data (demographic, strength, laxity and functional outcomes (Disability of the Arm, Shoulder, and Hand [QuickDASH]; Western Ontario Shoulder Instability Index; and QuickDASH Pain subscore) were compared at enrollment and last postoperative follow-up. Functional outcomes, complications, and postoperative outcomes were compared between the different groups and then subdivided by BMI. Follow-up radiographs were evaluated for graft position and complications for all patients who underwent OL. Results A total of 227 patients (164 men, 63 women) were included with at least 1-year follow-up (3.3y AB (n = 126), 4.5y ABR (n = 34), and 3y OL (n = 62)). At baseline, patients with high BMI (37(17%)) had significantly worse QuickDASH Pain subscores (2.9 ± 1.0, P value <.05) and QuickDASH (46.8 ± 21.6, P value < .001) scores compared to all other BMI groups. All BMI groups had similar QuickDASH ( P value .22) and Western Ontario Shoulder Instability Index ( P value .69) scores at last follow-up. Complication rates for patients with high BMI were significantly higher in ABR compared to AB ( P value .042) and so were reoperation rates in patients with high BMI after OL compared to patients with high BMI after ABR (9.5%, P value .049). Conclusion Patients with high BMI showed significantly worse baseline functional scores but no difference was found in postoperative functional scores between BMI groups. Complication rates were significantly higher in patients with high BMI following ABR compared to AB, and so were reoperation rates in patients with high BMI undergoing Latarjet compared to AB.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".