Interposition Graft Bridging Reconstruction of Irreparable Rotator Cuff Tears Using Acellular Dermal Matrix: Medium‐Term Results
Bibliographic record
Abstract
PURPOSE: In this study, we aimed to evaluate the medium-term clinical outcomes for patients who underwent bridging reconstruction. METHODS: A retrospective chart review was conducted for all patients who underwent bridging reconstruction between 2010 and 2018. Patients who were missing either pre- or postoperative outcome scores were excluded. All included patients completed self-reported questionnaires (Western Ontario Rotator Cuff [WORC] and Disabilities of the Arm, Shoulder and Hand [DASH]) pre- and postoperatively at 6 months, 1 year, and annually thereafter. All scores were reported out of 100. RESULTS: Ninety-one patients initially met our inclusion criteria, and 33 were excluded owing to lack of either pre- or postoperative outcome scores. Nine patients were lost to follow-up; therefore, 49 patients were finally evaluated, including 30 males (61.2%) and 19 females (38.8%) with an age of 59.6 ± 10.4 years (mean ± standard deviation) and mean follow-up of 5.3 years (range 2 to 9). Both WORC and DASH scores significantly improved from pre- to postoperatively (WORC: 69.6 ± 12.2 to 27.9 ± 23.7, P < .001; DASH: 51.5 ± 17.5 to 24.5 ± 23.0, P = .001). For WORC and DASH, 92% and 74% of patients, respectively, met the minimal clinical importance difference. CONCLUSION: Our results showed that patients' clinical outcome scores significantly improved with an average of 5-year follow-up, which demonstrates that bridging reconstruction is a safe procedure with promising midterm clinical outcomes. LEVEL OF EVIDENCE: IV, retrospective case series.
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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".