Massive Rotator Cuff Tear Bridging Reconstruction Using Acellular Dermal Matrix: Mid-term Results (219)
Bibliographic record
Abstract
Objectives: Management of massive rotator cuff tears remains a challenge despite development of different surgical techniques. Bridging reconstruction was developed to improve clinical outcomes and to avoid the morbidity associated with tendon transfers and arthroplasty. To date, there have been no studies assessing the mid-term clinical outcome for patients undergoing this procedure. The objective of this study was to evaluate the mid-term clinical outcomes of patients who underwent bridging reconstruction using acellular dermal matrix for large/massive rotator cuff tears (> 3 cm). Methods: A retrospective chart review was conducted for patients who underwent bridging reconstruction between 2010 and 2018 by one surgeon (IW). Patients with a minimum follow-up of two years were included. All patients completed self-reported questionnaires (Western Ontario Rotator Cuff (WORC) Index and the Disabilities of the Shoulder, Arm and Hand (DASH) score) pre-operatively and post-operatively at six months, one year and annually thereafter. Results: One hundred charts were reviewed, and 20 patients were excluded due to lack of pre- or post-operative outcome scores. This cohort of patients consisted of 80 patients mean age of 58.6 ± 9.1 years and an average follow-up of 5.1 ± 1.6 years. The mean pre- and post-operative WORC scores (± SD) were 61.4 ± 20.6 and 28.1 ± 25.5 (p < 0.001), respectively. The mean pre- and post-operative DASH scores were 52.2 ± 19.7 and 23.1 ± 22.4 (p < 0.001), respectively. There was an improvement in WORC and DASH over time with the most significant improvement during the first year post-operatively (Figure 1). Seventy-eight percent of patients met the minimal clinically important difference (MCID) for WORC while 77% of patients met the MCID for DASH. Our results show significant mid-term clinical improvement in WORC and DASH scores that is similar to previously published data that showed significant improvements in other patient-reported outcome scores. We believe these positive outcomes to be a result of better force coupling within the shoulder as compared to other surgical treatments such as maximal repair, superior capsular reconstruction, and reverse total shoulder arthroplasty. Conclusions: Arthroscopic bridging reconstruction for the treatment of massive rotator cuff tears results in significant improvement of patient reported outcomes (as measured using the WORC and DASH scores) at a mean follow up of five years. This may suggest that bridging reconstruction is a good treatment alternative as compared to superior capsular reconstruction or reverse total shoulder arthroplasty. Longer follow-up with a prospective study design is necessary to determine the longevity of these 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".