Comparison of Outcomes after Arthroscopic Rotator Cuff Repair between Elderly and Younger Patient Groups: A Systematic Review and Meta-Analysis of Comparative Studies
Bibliographic record
Abstract
This study aimed to compare the outcomes of arthroscopic rotator cuff repair (ARCR) surgery between younger and older patients. We performed this systematic review and meta-analysis of cohort studies comparing outcomes between patients older than 65 to 70 years and a younger group following arthroscopic rotator cuff repair surgery. We searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and other sources for relevant studies up to 13 September 2022, and then assessed the quality of included studies using the Newcastle-Ottawa Scale (NOS). We used random-effects meta-analysis for data synthesis. The primary outcomes were pain and shoulder functions, while secondary outcomes included re-tear rate, shoulder range of motion (ROM), abduction muscle power, quality of life, and complications. Five non-randomized controlled trials, with 671 participants (197 older and 474 younger patients), were included. The quality of the studies was all fairly good, with NOS scores ≥ 7. The results showed no significant differences between the older and younger groups in terms of Constant score improvement, re-tear rate, or other outcomes such as pain level improvement, muscle power, and shoulder ROM. These findings suggest that ARCR surgery in older patients can achieve a non-inferior healing rate and shoulder function compared to younger patients.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".