Double‐bundle ACL reconstruction: how big is the learning curve?
Bibliographic record
Abstract
The study aim was to determine whether an experienced ACL surgeon could convert from a single-bundle to a double-bundle technique with relative accuracy. We also wanted to determine whether there was a significant learning curve. Ten double-bundle ACL reconstruction procedures were carried out on suitable individuals. Following the procedure, all patients underwent a CT scan of the relevant knee. Femoral and tibial tunnel locations were then measured and compared to reference anatomical locations previously described in the literature. The results were not known to the surgeon until all 10 cases were completed. The total percentage difference between the sum of all four study tunnel locations from their reference anatomical positions was calculated for each patient to assess overall accuracy in tunnel placement. Surgical time and all complications were recorded. There were no complications. The surgical time for patient 1 was 125 min and 65 min for patient 10. There was a tendency to place the anteromedial tunnel on the femur more distal than its anatomical location. The femoral posterolateral tunnel position was placed distal to its anatomical location in all cases. As a consequence, it was also slightly anterior compared to its anatomical location. Accurate tibial tunnel placement was achieved for both the AM and the PL tunnels. An improvement in tunnel placement was observed over the 10 cases. This present study shows that it is possible for an experienced ACL surgeon to convert from a transtibial single-bundle technique to a medial portal double-bundle reconstruction with relative accuracy.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".