Supraspinatus tendon transosseous vs anchor repair surgery: a comparative study of mechanical recovery in the rabbit
Bibliographic record
Abstract
BACKGROUND: Supraspinatus (SSP) tendon ruptures requiring surgical repair are common. Arthroscopic suture anchor fixation has gradually replaced transosseous repair in supraspinatus tendon tear. Our objective was to compare mechanical properties between transosseous and anchor supraspinatus repair in the first 6 postoperative weeks in a rabbit model. METHODS: One hundred and fifty-two rabbits had one supraspinatus tendon repaired either with an anchor suture 1 week after detachment or with transosseous sutures. Rabbits were euthanized at 0, 1, 2, 4 or 6 postoperative weeks. Experimental and contralateral tendons (304 tendons) were mechanically tested to failure. Data are expressed as percent of contralateral. RESULTS: Anchor repair had higher loads to failure compared to transosseous repair, at immediate repair (week 0, 52 ± 21% vs 25 ± 17%, respectively; p = 0.004) and at 1 postoperative week (64 ± 32% vs 28 ± 10%; p = 0.003) with no difference after 2 weeks. There was no difference in stiffness. Transosseous repairs showed higher rates of midsubstance failures compared to anchor repairs at 1 (p = 0.004) and 2 postoperative weeks (p < 0.001). Both transosseous and anchor repairs restored supraspinatus mechanical properties after 4 postoperative weeks. CONCLUSION: Anchor repair provided better initial tensile strength while transosseous repair led to a faster normalization (namely, midsubstance) of the mode of failure. Research to optimize supraspinatus repair may need to consider the advantages from both surgical approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".