Assessing the accuracy of femoral tunnel placement in anatomic ACL reconstruction (913.13)
Bibliographic record
Abstract
Current trends in Anterior Cruciate Ligament (ACL) Reconstruction favor anatomic positioning of ACL attachment sites. Surgical inaccuracy in femoral tunnel positioning can lead to potential early graft failure and early‐onset osteoarthritis. The purpose of this study was to evaluate accuracy of femoral tunnel positioning by orthopedic surgeons using five ACL femoral guides. Using a human cadaveric femur, the mathematical center of the ACL femoral attachment was determined to be the anatomically ideal location for femoral tunnel placement. Five orthopedic surgeons performed arthroscopic femoral tunnel pin placement on identical artificial femurs using 5 different femoral guides (ConMed TM Linvatec Offset & Bullseye TM , Arthrex® Transportal, Stryker® VersiTomic®, Smith&Nephew CLANCY TM ). Tunnel placement was compared to the ideal position using virtual Computer Tomography. A total of 125 femurs were used. The Stryker® guide had greatest accuracy (distance from ideal footprint) at 3.9mm, and the most optimal tunnel length at 33.4mm. Only 8/125 iterations were within our set acceptable limit of 2mm from the ideal footprint. Tremendous variability of femoral tunnel positioning was observed with each guide. Only 6.4% of trials fell within our acceptable limit. Complimentary research is needed in cadavers and humans to fully elucidate anatomic accuracy and applicability to the operating room.
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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 | 0.005 | 0.012 |
| 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".