Matrix Molecule mRNA Levels in the Bursa and Rotator Cuff of Patients With Full‐Thickness Rotator Cuff Tears
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate at the mRNA level a subset of extracellular matrix molecules relevant during healing and remodeling of rotator cuff tears. TYPE OF STUDY: Controlled laboratory study. METHODS: Bursal and rotator cuff tissue from the margin of the rotator cuff tear were harvested from 10 patients (mean age, 57.5 +/- 7.3 years) undergoing surgical repair of full-thickness rotator cuff tears. There were six male and four female patients with a mean duration of symptoms of 14.6 months (range, 2 to 60 months). The mean tear size was 4.4 cm. In addition, tissue was obtained from 6 cadaveric specimens with no gross evidence of rotator cuff tearing. Reverse transcription polymerase chain reaction (RT-PCR) was performed for type I, II, and III collagen, biglycan, decorin, and aggrecan, and normalized to the housekeeping gene GAPDH. RESULTS: RT-PCR showed that both the bursa and rotator cuff margin had increased mRNA levels for type I and type III collagen in patients with full-thickness rotator cuff tears when compared with normal cadaveric controls. In addition, there was a significant decrease in decorin mRNA levels and an increase in aggrecan mRNA levels in the rotator cuff margin of torn rotator cuff tendons when compared with normal cadaveric controls. CONCLUSIONS: These results suggest that both the bursa and rotator cuff margin of patients with rotator cuff tears are actively remodeling after injury and that both tissues may potentially contribute to the healing process following repair. CLINICAL RELEVANCE: These findings may help clinicians determine what aspects of the repair process can be manipulated to affect optimal ruptured tendon 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".