Biceps Tenodesis for the Treatment of Type II Superior Labral Anterior Posterior (SLAP) Tears in Overhead Athletes Under the Age of 35: A Case Series
Bibliographic record
Abstract
BACKGROUND: The best treatment for type II superior labral anterior posterior (SLAP) tears in overhead athletes is not well defined. QUESTIONS/PURPOSE: The purpose of this study was to examine post-surgical outcomes in overhead athletes under the age of 35 who underwent primary biceps tenodesis for an isolated type II SLAP tear. We hypothesized that these patients would have high rates of return to play, as well as recovery of range of motion (ROM) and strength after surgery. PATIENTS AND METHODS: Patients were between the ages of 18 and 35, had a primary isolated type II SLAP tear confirmed on magnetic resonance imaging (MRI), and were injured performing overhead activities. All patients underwent biceps tenodesis using an arthroscopic suprapectoral approach. Patients underwent standard postoperative rehabilitation lasting up to one year. Function and outcomes were measured at baseline, three months, six months, one year, and two years using range of motion, strength testing, and patient-reported outcomes (PROs) (Western Ontario Shoulder Instability Index (WOSI), Kerlan-Jobe Orthopedic Clinic Score (KJOC), American Shoulder and Elbow Surgeons Assessment Form (ASES), and Single Assessment Numeric Evaluation (SANE)). RESULTS: Five patients were included in the case series. There was consistent improvement at each time point on PROs: WOSI, p=0.01; KJOC, p=0.04; SANE, p=0.02; and ASES, p=0.03. Range of motion increased from baseline to each time point with a significant improvement in forward flexion (p=0.03). In strength testing, there were improvements in all exercises and a significant improvement in abducted external rotation between years 1 and 2 (p<0.01). CONCLUSIONS: This study demonstrated that biceps tenodesis in overhead athletes under the age of 35 provides improved outcomes, ROM, and strength.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".