When Do Rotator Cuff Repairs Fail? Serial Ultrasound Examination After Arthroscopic Repair of Large and Massive Rotator Cuff Tears
Bibliographic record
Abstract
BACKGROUND: Despite advances in arthroscopic repair of rotator cuff tears, recurrent tears after repair of large and massive tears remain a significant clinical problem. The primary objective of this study was to define the timing of structural failure of surgically repaired large and massive rotator cuff tears by serial imaging with ultrasound. The secondary objective of this study was to investigate the association between recurrent tears and clinical outcome after rotator cuff repair. HYPOTHESIS: Recurrent tear after arthroscopic repair of large rotator cuff tears is more likely to occur late (>3 months) in the postoperative period and will be associated with inferior clinical outcome scores. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: Twenty-two consecutive patients with large (>3 cm) rotator cuff tears underwent arthroscopic repair with a standardized technique. Serial ultrasound examinations were performed at 2 days, 2 weeks, 6 weeks, 3 months, 6 months, 12 months, and 24 months after surgery. Western Ontario Rotator Cuff (WORC) Index scores were also collected at these time points. RESULTS: Nine (41%) of the 22 arthroscopically repaired rotator cuff tears demonstrated recurrent tears. Seven of the 9 retears occurred within 3 months of surgery, and the other 2 occurred between 3 and 6 months. No retears occurred after 6 months. At 24-month follow-up, WORC scores favoring intact rotator cuffs over retears approached statistical significance (mean WORC intact 123.9 vs retear 659.8; P = .07). CONCLUSION: Recurrent rotator cuff tears are not uncommon after arthroscopic repair of large and massive tears. These recurrent tears appear to occur more frequently in the early postoperative period (within the first 3 months) and are associated with inferior clinical outcomes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".