Does Increased Body Mass Index Influence Outcomes After Rotator Cuff Repair?
Bibliographic record
Abstract
PURPOSE: To investigate the influence of pre-existing obesity (body mass index [BMI] ≥ 30) on outcomes after rotator cuff repair surgery. METHODS: We collected data on adult patients who underwent surgical repair for symptomatic full-thickness rotator cuff tears confirmed by imaging between 2012 and 2015. The required follow-up was 3 years. At baseline and 6, 12, 24, and 36 months, the American Shoulder and Elbow Surgeons score, Western Ontario Rotator Cuff index, and visual analog scale pain scores were collected. Complications were assessed by a chart review. Obesity was defined as BMI ≥ 30. Chi-square analysis and Student's t-test examined differences between categorical and continuous variables at baseline. Generalized estimating equations examined the effects of fixed factors on outcome variables longitudinally from baseline to 36 months. RESULTS: Thirty-nine percent of 213 subjects were obese (mean BMI = 29.2; range, 16-48; standard deviation, 5.8). There were no statistically significant differences between obese and nonobese subjects in other baseline characteristics. When controlling for covariates, obese subjects reported no differences in Western Ontario Rotator Cuff, American Shoulder and Elbow Surgeons, or visual analog scale pain scores when compared with nonobese subjects at baseline and over 3 years from surgery. Although obese patients were more likely to have inpatient surgery, there was no difference in the incidence of postoperative complications. CONCLUSIONS: Contrary to our hypothesis, obese participants who underwent rotator cuff repair reported no difference in functional outcome or pain scores compared with nonobese participants over 3 years. In addition, obesity was not associated with postoperative complications in this study. However, as we hypothesized, obese participants were more likely than nonobese participants to have repair in the inpatient setting. LEVEL OF EVIDENCE: Level III, retrospective comparative study.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| 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".