Soft-tissue quadriceps tendon autograft during primary anterior cruciate ligament reconstruction in Skeletally-immature patients vs. Hamstrings: A protocol for a multi-centre randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: ACL tears in skeletally-immature patients are often treated surgically using soft-tissue autografts to offset increased risks of cartilage, meniscus and physeal injuries. Despite technological innovation, there remains a high failure rate among active young patients. We present a randomized controlled trial (RCT) to evaluate the soft-tissue quadriceps autograft compared to the standard hamstring autograft to treat primary ACL tears in the pediatric population. METHODS: This international RCT will evaluate 352 skeletally-immature patients between the ages of 10-18 years undergoing primary ACL reconstruction to compare the effect of hamstring versus soft-tissue quadriceps autografts on ACL failure rate as a primary outcome, and return to sport, knee function, knee pain, health-related quality of life and health utility, range of motion and stability, and other adverse events at 24 months as secondary outcomes. Follow-up will occur at 6 weeks and 6, 12, 18, and 24 months postoperatively. DISCUSSION: This trial will inform the optimal primary surgical management for ACL injuries as it pertains to all soft-tissue graft selection, to not only reduce the relatively high failure rates, but also improve the function and return-to-sports in this young, active population. TRIAL REGISTRATION: This trial was registered on ClinicalTrials.gov (NCT03896464) on March 27th, 2019.
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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 | 0.032 | 0.029 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".