Comparison of arthroscopic suture-bridge technique and double-row technique for treating rotator cuff tears
Bibliographic record
Abstract
BACKGROUND: Rotator cuff tear is a common shoulder disorder in the elderly. Either arthroscopic double-row (DR) or suture-bridge (SB) technique for rotator cuff tear patients is needed to choose. We conducted this systematic review and meta-analysis to compare the clinical outcomes of arthroscopic SB versus DR intervention. METHODS: The 7 studies were acquired from PubMed, Medline, Embase, CNKI, Google, and Cochrane Library. The data were extracted by 2 of the co-authors independently and were analyzed by RevMan5.3. Mean differences (MDs), odds ratios (ORs), and 95% confidence intervals (CIs) were calculated. Cochrane Collaboration's Risk of Bias Tool and Newcastle-Ottawa scale were used to assess risk of bias. RESULTS: Seven studies including 1 randomized controlled trial and 6 observational studies were assessed. The methodological quality of the trials ranged from low to moderate. The pooled results of American Shoulder and Elbow Surgeons score, Constant score, visual analog scale score, and range of motion showed that the differences were not statistically significant between the 2 interventions. The difference of University of California at Los Angeles (UCLA) score was statistically significant between SB and DR intervention, and SB treatment was more effective (MD = -0.95, 95% CI = -1.70 to -0.20, P = .01). The difference of re-tear rate was statistically significant and SB treatment achieved better result than DR treatment (OR = 0.31, 95% CI = 0.15-0.64, P = .001). Sensitivity analysis proved the stability of the pooled results and the publication bias was not apparent. CONCLUSIONS: Both arthroscopic SB and DR interventions had benefits in rotator cuff tear. SB treatment was more effective in UCLA score and had lower re-tear rate than DR treatment. The arthroscopic SB technique is recommended as the optical choice for rotator cuff tear.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".