Muscle tissue atrophy, extramuscular and intramuscular fat accumulation, and fat gradient after delayed repair of the supraspinatus tendon: A comparative study in the rabbit
Bibliographic record
Abstract
To investigate the atrophy of supraspinatus (SSP) muscle tissue and accumulation of extramuscular fat (e-fat) and intramuscular fat (i-fat) after delayed repair of the SSP tendon, and to correlate CT findings with histology. One SSP tendon of 36 rabbits was transected, then repaired in groups of 12 at 4, 8, or 12 weeks and then followed for 12 weeks. Thirty-six normal shoulders served as controls. We compared the SSP muscle, e-fat weights and volumes, muscle tissue and i-fat areas on histology, e-fat and attenuation values on CT between the experimental and control shoulders. CT-to-histology correlations were run. SSP muscle tissue atrophy, e-fat and i-fat accumulation were present after tendon repair delayed by 4, 8, or 12 weeks (all p < 0.05). Both e-fat and i-fat accumulation presented increasing proximal-to-distal gradients (both p < 0.05). CT depicted the muscle tissue atrophy, e-fat and i-fat accumulation, and increasing proximal-to-distal gradients (all p < 0.05). We found strong CT-to-histology correlations (p < 0.05). SSP muscles showed tissue atrophy, e-fat and i-fat accumulation after successful SSP tendon repairs delayed by 4, 8, or 12 weeks. Subtracting i-fat from the muscle area determined the SSP muscle tissue atrophy. Increasing proximal-to-distal fat gradients were diagnostic if not pathognomonic of an initial SSP tendon disruption.
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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 | 0.000 |
| 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 | 0.001 |
| 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".