Management of Failed SLAP Repair: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Superior labrum anterior to posterior (SLAP) tears are a very common shoulder injury. The success rate of SLAP repair, particularly in the throwing athlete, has been variable in the literature. QUESTIONS/PURPOSES: The purpose of this systematic review was to evaluate the reported post-operative outcomes of management techniques for failed SLAP repair. METHODS: The electronic databases MEDLINE, Embase, and PubMed were searched for relevant studies, and pertinent data was abstracted. Only studies reporting outcomes of management techniques for failed SLAP repairs were included. RESULTS: A total of 10 studies (levels III to IV) evaluating 176 patients were included in this systematic review. Most subjects were male (86.6%), with a mean age at surgery of 36.3 years (range, 17 to 67 years). The most commonly reported reason for failed SLAP repair was persistent post-operative mechanical symptoms after index SLAP repair. Common techniques used in the management of failed SLAP repair include biceps tenodesis and revision SLAP repair. Return to activity was significantly higher after biceps tenodesis than after arthroscopic revision SLAP repair. However, compared to primary SLAP repair, biceps tenodesis demonstrated no statistically significant differences in return to work rates. Complications reported in one case were resolved post-operatively, and there was no reported revision failure or reoperation after revision surgery. CONCLUSION: The most common reason for failed SLAP repair is persistent post-operative mechanical symptoms. Revision surgery for failed SLAP repair has a high success rate. The rate of return to activity after biceps tenodesis was significantly higher than the rate after revision SLAP repair. Large high-quality randomized trials are required to provide definitive evidence to support the optimal treatment for failed SLAP repair.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".