Does the Upward Migration Index Predict Function and Quality of Life in Arthroscopic Rotator Cuff Repair?
Bibliographic record
Abstract
BACKGROUND: Although upward humeral head migration is a well-recognized phenomenon in patients with tears of the cuff, it is unclear whether it relates to patient function after cuff repair. The upward migration index (UMI) assesses proximal migration of the humeral head while controlling for patients' bony morphologic features. QUESTIONS/PURPOSES: We asked whether functional and quality-of-life (QOL) improvement occurs longitudinally in patients with low, moderate, or high degrees of proximal humeral migration after arthroscopic cuff repair and whether differences occur between groups. PATIENTS AND METHODS: We retrospectively reviewed 118 patients with full-thickness tears treated by arthroscopic cuff repair. Patients were divided into three groups depending on the severity of preoperative proximal humeral migration seen on MRI. We determined function using two functional scores and the Western Ontario Rotator Cuff Index (a QOL index). Evaluations were performed preoperatively and 6 and 12 months postoperatively. A general linear model analysis controlled for patient characteristics, including the UMI, to determine their effects on functional and QOL scores. RESULTS: Function and QOL improved after surgery in all three groups. The UMI did not correlate with final functional or QOL scores. Six-month functional and QOL scores correlated with final scores. The best predictor of final strength was initial strength. CONCLUSIONS: Preoperative UMI did not correlate with functional or QOL improvements after surgery. The data suggest substantial proximal migration of the humeral head, as measured by the UMI, should not be considered a contraindication to arthroscopic rotator cuff 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".