Superior labral anterior-posterior (SLAP) tears: recent advances and outcomes
Bibliographic record
Abstract
Purpose of review The study presents a concise review of the published literature of disorders of the superior labrum, with a focus on reports published within the past 2 years. The aim of this review is to assist physicians with their assessment and treatment of lesions of the superior labrum. Recent findings The superior labrobicipital complex has many anatomical variations, some of which may be confused with superior labral anterior-posterior lesions. Various theories exist for potential mechanisms of injury to the superior labrum. Progress has been made into understanding the aspects involved in making a clinical diagnosis of a superior labral anterior-posterior lesion, with the physician's appreciation of the overall patient profile seemingly more important than any specific physical test. Imaging of the superior labrum continues to evolve. Research into the interobserver reliability of the arthroscopic diagnosis of a superior labral anterior-posterior lesion has demonstrated a poor level of agreement between clinicians. Surgical techniques for the treatment of superior labral anterior-posterior lesions and supraglenoid cysts continue to evolve. Summary Recent research has provided insights into understanding disorders of the labrobicipital complex. A clearer understanding of issues including normal age-related anatomy and function of the superior labrobicipital complex will aid in the diagnosis and treatment of superior labral anterior-posterior lesions.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".