Is there significant variation in the material properties of four different allografts implanted for ACL reconstruction
Bibliographic record
Abstract
The aims of our study were to: (1) determine if there are differences in the material properties of tendon obtained from implanted tibialis anterior, achilles, bone-patella- bone and tibialis posterior allografts; (2) determine the variability in material properties between the implanted specimens. A total of 60 specimens were collected from fresh frozen allografts implanted at ACL reconstruction. Specimens collected included 15 tibialis anterior, 15 tibialis posterior, 15 achilles and 15 bone-patella-bone tendons. Each specimen was mounted in a custom made cryogrip. The mounted specimens were loaded onto a MTS Testline servo-hydraulic testing machine in a uni-axial tensile test configuration. Specimens were subjected to a strain rate of 5% per second until the ultimate tensile stress (UTS), failure strain and high strain modulus was calculated for each specimen after being normalized for specimen dimensions. Individual material properties were tested using one way analysis of variance (ANOVA) and post hoc Tukey's B test with a P value of <0.05 considered significant. Homogeneity of variance was assessed using the Levene's test. As a result, no significant difference was found between all four grafts with regards to UTS, failure strain or high strain linear modulus. The UTS was plotted against the modulus demonstrating a linear relationship which is typical of soft tissues. Significant variability in the results were observed. In conclusion, there was no significant statistical difference between the material properties of the four tendon allografts tested. But significant variability in results was observed within groups and between groups, which may provide one explanation for the range of results in allograft ACL reconstruction reported in the literature.
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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 | 0.002 |
| 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 | 0.000 |
| 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".