Labral Repair Versus Biceps Tenodesis for Primary Surgical Management of Type II Superior Labrum Anterior to Posterior Tears: A Systematic Review
Bibliographic record
Abstract
PURPOSE: To examine the outcomes of SLAP repair versus biceps tenodesis (BT) for the index treatment of isolated type II SLAP tears. METHODS: A search of PubMed, MEDLINE, and EMBASE was performed in April 2018 for English-language studies that presented outcomes data on patients with isolated type II SLAP tears treated with either SLAP repair or BT at the primary surgical time point. RESULTS: Twenty-three studies (i.e., 2 randomized control trials, 7 retrospective cohort, 3 prospective cohort, 4 case-control, and 7 case series) were included. Isolated type II SLAP tears were treated via SLAP repair in 781 patients with a mean age of 35 years (range, 22-58 years) and a mean postoperative follow-up of 35 months (range, 3-63 months). BT was performed in 100 patients with a mean age of 44 years (range, 18-64 years) and a mean postoperative follow-up of 32 months (range, 24-75 months). Similar postoperative scores were noted in both the SLAP repair and BT groups for American Shoulder and Elbow Surgeons, Constant, University of California, Los Angeles, and visual analog scale pain scores. The rate of return to sports was 20% to 95% for SLAP repair and 73% to 100% for BT. Reoperation rates for SLAP repair and BT were 2.9% to 40% and 0% to 15.3%, respectively. CONCLUSIONS: This study suggests that SLAP repair and BT are both acceptable as index treatment for isolated type II SLAP tears. SLAP repair remains the most commonly performed index procedure; however, BT appears equally efficacious and may represent an attractive alternative. LEVEL OF EVIDENCE: Level IV, systematic review of Level I through IV studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".