Healing rate and clinical outcomes of xenografts appear to be inferior when compared to other graft material in rotator cuff repair: a meta-analysis
Bibliographic record
Abstract
Importance The use of grafts is a viable option for larger rotator cuff tears. Graft types can be divided into autograft (AU), allograft (AL), xenograft (XE) and synthetic (SY) material. However, the outcomes of each graft type are diverse. Objective The objective of the study was to compare the healing rate and clinical outcomes of different graft materials. Evidence review A systematic literature review was performed, and clinical studies of rotator cuff repair using grafts as augmentation or bridging were included. The primary outcome was tendon healing. The secondary outcomes included Visual Analogue Scale, American Shoulder and Elbow Surgeons Score and University of California at Los Angeles (UCLA) Score and forward elevation range. The studies were divided into AU, AL, XE and SY groups and compared. Analysis was a random effects model for healing rates and fixed effects models weighted by sample size for all the other measures. Findings 23 studies with 25 study groups were included. A total of 130 (AU), 205 (AL), 111 (XE) and 202 (SY) repairs (mean age 63.7, 57.4, 63.5 and 63.7 years; mean follow-up 25.3 27.4, 28.6 and 26.4 months, respectively) were analysed. The estimated healing rates were 62.9% (AU), 78.9% (AL), 46.8% (XE) and 77.2% (SY), respectively. While no statistical significance was detected, the healing rate of XE was lower by more than 30% than AL and SY. The improvement of UCLA in XE was significantly less than the other 3 groups. Conclusions and relevance The healing rate and clinical outcomes of XE appear to be inferior, however, the results varied. While graft type affects clinical outcomes, other factors may also be as important. Level of evidence IV.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.054 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".