Surgical and Non-Surgical Interventions in Complete Rotator Cuff Tears
Bibliographic record
Abstract
BACKGROUND: This systematic review compares the efficacy and safety of surgical and non-surgical interventions for full- thickness rotator cuff tears. METHODS: A systematic literature search was conducted in five databases. Randomized (RCTs) and non-randomized controlled trials of interventions (non-RCTs) for the surgical or non-surgical treatment of patients with traumatic or atraumatic full-thickness rotator cuff tears were included. The review protocol was published in the PROSPERO registry (CRD42018100343). RESULTS: Ten studies (three RCTs with 332 participants; seven non-RCTs with 650 participants) met the inclusion criteria. One year after treatment, shoulder function, measured with the 100-point Constant score, had improved by 6.7 points (95% confidence interval [2.3; 11.0]) and pain, measured with the 10-cm visual analog scale, by 1.1 cm (0.5; 1.7] in the full-thickness rotator cuff tears treated surgically compared with non-surgical treatment. In one study the difference in favor of surgery persisted after 10 years' follow-up. For other outcomes, such as range of motion, muscle strength, quality of life, and adverse events, the data were sparse and the group differences were similar. The findings of the non-RCTs were comparable with those of the RCTs. CONCLUSION: With regard to functional improvement and pain reduction, surgical treatment of full-thickness rotator cuff tears was superior to non-surgical treatment in the short and the long term. Whether the differences between the groups are relevant for individual cases is uncertain, as the measured results were distributed below and above the threshold of clinical relevance. The conclusions may not be applicable to rotator cuff tears over 3 cm in size or to young persons.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".