Is Posterior Tibial Slope and Mechanism of Failure Crucial for an Anatomically Reconstructed Primary Hamstring Graft Anterior Cruciate Ligament?
Bibliographic record
Abstract
Purpose Native anterior cruciate ligament (ACL) failure is multifactorial with tibial slope identified as a crucial risk factor. The aim was to examine relation between lateral posterior tibial slope (LTPS) and failed ACL primary reconstruction by negating the associated risk factors such as tunnel position, gender, and graft types based on the mechanism of failure. Materials and Methods Our retrospective study included 102 patients, diagnosed as failed primary anterior cruciate ligament reconstruction (ACLR). The LPTS was measured on lateral radiographs and the tunnel position assessed by magnetic resonance imaging on both femoral, tibial side by two musculoskeletal radiologists. We compared the slopes in patients based on their mechanism of failure. Results The mean LPTS in patients with anatomically placed tunnel (9.28° ± 3.5°; range, 4°–18°) was significantly higher than the rest (7.7° ± 2.9°; range, 3°–15°; P = 0.01). There was a significant association of higher tibial slope in graft rupture due to contact mechanism of failure ( P = 0.02). LPTS was not significantly associated with noncontact mechanism of failure. Conclusion LTPS is a significant risk factor for failure in hamstring graft reconstructed ACL patients with optimally placed tunnels. LPTS ≥10° increases the risk of hamstring graft failure due to contact mechanism.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".