Exploring cellular pathology after rotator cuff tears: implications of a shift towards fast fibers and increase in angiogenesis for repair and rehabilitation
Bibliographic record
Abstract
Supraspinatus tears are common in orthopaedic clinical practice but there is a gap in the literature regarding the natural history of rotator cuff pathology which compromises our ability to predict the potential recovery success following surgical repair. Previous studies have examined fatty infiltration, atrophy, MRI occupancy ratio, tangent sign, and Goutallier scores as predictors for the functional recovery of muscle, but there are scarce data investigating the cellular and molecular pathology of rotator cuff tears. This study investigated the fiber-type composition and myosin heavy chain protein (MyHC) (I/slow, II/fast: IIa, IIx, IIb) and vascular endothelial growth factor (VEGF) of pathological supraspinatus via immunohistochemistry and western blot. Corresponding clinical and MRI data were used to identify factors that may influence recovery. Biopsies (n=27) from torn supraspinatus and control muscle (deltoid) were retrieved during arthroscopic rotator cuff repair surgery. Torn supraspinatus demonstrated 44 ± 1% slow fibers vs. 57 ± 1% in control muscle (p <0.01), while the proportion of intermediate or transitional fibers (mixed positive and negative staining) did not differ (3.4 ± 0.4% in supraspinatus, vs. 2.8 ± 0.3% in control). Differences in sex, smoking history, time from injury to surgery, and MRI data did not correlate with fiber type or myosin. VEGF was higher in pathological muscle (p <0.05). A shift towards fast fibers and greater VEGF suggests supraspinatus injury, consistent with disuse and/or denervation. These results together with muscle innervation status can provide insight into the overall health of the rotator cuff and allow us to better predict its ability to recover following injury.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".