Supraspinatus tendon repair into a bony trough in the rabbit: Mechanical restoration and correlative imaging
Bibliographic record
Abstract
Recurrence of tears is a common complication after rotator cuff surgery. Retearing seems to occur early after surgery and may be attributed to too early or too vigorous exercises. We found no experimental data correlating the strength of the rotator cuff early after surgery and imaging. Our objectives were to measure the peak load to failure of rabbit supraspinatus tendon-bone constructs at early times postoperatively, to determine their mode of failure, and to determine whether computed tomography (CT) can predict their strength. We divided one supraspinatus tendon of 40 adult female white New Zealand rabbits and, after resection of the enthesis, sutured the tendon into a bony trough. Ten rabbits were killed immediately and 10 each at 1, 2, and 6 weeks postoperatively. The explanted tendons of both shoulders were imaged on CT and tested to failure. Compared to normal tendons (mean 210 +/- 42 N), the mean strength was very low at 0 weeks (57 +/- 21 N) and 1 week (86 +/- 33 N) (both p < 0.05); it had recovered by 6 weeks (324 +/- 66 N). Early on, suture pullout was the most common mode of failure, whereas at 6 weeks, mid-substance tears predominated (p < 0.05). Hypoattenuation on CT was associated with increased strength of the tendon-bone construct (p < 0.05). The strength of the surgical construct is very low in the early postoperative period. Therefore, the shoulder should be submitted only to loads not interfering with healing.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".