Effect of timing of surgical SSP tendon repair on muscle alterations
Bibliographic record
Abstract
To investigate the impacts of delayed repairs of a supraspinatus tendon tear on the supraspinatus muscle, we used an animal model data from two previously published studies in which one supraspinatus (SSP) tendon was detached. In one cohort, the rabbits were killed in groups of 10 at 4, 8, and 12 weeks. In the other cohort, a repair was done at these time points, 12 rabbits each, and the animals killed were 12 weeks later. SSP fossa volume (Muscle belly plus extramuscular fat [e-fat] volume), percentage of intramuscular fat (i-fat), and muscle tissue volume (muscle belly volume minus i-fat), as well as CT determination of e-fat and i-fat of both cohorts, were compared. Fossa volume increased (p < 0.05). Muscle belly and muscle tissue volumes did not increase after repair (p > 0.05), but early repair prevented further volume losses, a fact not seen after 8 and 12 weeks delay of repair. No reversal of e-fat or of i-fat occurred, in fact i-fat almost doubled after 4 weeks delay of repair (p < 0.05). CT studies confirmed the fat results. We conclude that early repair prevented loss of muscle belly and muscle tissue volumes, but that it has no positive influence on fat accumulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".