Primary arthroscopic repair of massive rotator cuff tears results in significant improvements with low rate of re‐tear
Bibliographic record
Abstract
PURPOSE: To conduct a systematic review of outcomes following primary arthroscopic repair of chronic massive rotator cuff tears (RCTs) and to assess clinical outcomes and rates of repair failure. The authors' preferred treatment algorithm is also provided. METHODS: Medline, Embase and PubMed were searched identifying articles pertaining to primary arthroscopic repair of chronic massive RCTs without the use of augmentation. Primary outcomes were patient-reported outcomes and the secondary outcome was the rate of repair failure. Outcome data were pooled and presented as well as assessment of study methodological quality. Data from studies reporting similar outcome measures were pooled when possible, and mean differences alongside confidence intervals and p values were reported, where appropriate. RESULTS: Twenty-six studies (1405 participants) were included, with mean age of 62 years (range 52-69). The mean duration of symptoms pre-operatively was 31 months (range 6-40), and the mean follow-up time was 39 months (range 12-111). Complete repair was performed in 78% of patients and partial repair was performed in 22%. Both complete and partial repairs resulted in significant improvements with respect to pain, range of motion and functional outcome scores. The rate of repair failure for the total cohort was 36% at a mean follow-up of 31 months, and for the complete and partial repair subgroups the failure rate was 35% and 40%, respectively. CONCLUSIONS: Arthroscopic repairs of chronic, massive RCTs, whether complete or partial, are associated with significant improvements in pain, function and objective outcome scores. The rate of repair failure is lower than previously reported, however, still high at 36%. The present paper finds that arthroscopic repair is still a viable treatment option for massive RCTs. 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 | 0.014 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".