Accuracy and Learning Curve of Femoral Tunnel Placement in Medial Patellofemoral Ligament Reconstruction
Bibliographic record
Abstract
Abstract The purpose of this study was to assess the accuracy of femoral tunnel placement in a medial patellofemoral ligament reconstruction (MPFL-R) cohort. The secondary purpose was to establish the evidence of a learning curve to achieve acceptable femoral tunnel placement during MPFL-R. Two surgeons, using lateral radiographs, assessed 73 subjects post–MPFL-R. Femoral tunnel accuracy and direction of tunnel error were measured in relation to Schöttle's point (A–T distance). Interrater reliability (intraclass correlation coefficient 2,k) of these measures was calculated. Learning curve of accurate femoral tunnel placement was examined by dividing the patient cohort into quartiles. A one-way analysis of variance was used to assess the quartiles for accuracy of femoral tunnel position and surgical time. In relation to Schöttle's point, 66/73 (90.4%) femoral tunnels were categorized as being in a “good” or “excellent” position and 7/73 (9.6%) were categorized as being in a “poor” position. Evidence of an MPFL-R learning curve was established via a statistically significant difference in the mean A to T distance for the four quartiles (F [3, 69] = 6.41, p = 0.001). There was also a statistically significant difference in the surgical time for the four quartiles (F [3, 69] = 8.71, p = 0.001). In this series, accurate femoral tunnels were placed more than 90% of the time during MPFL-R. A clear learning curve for accurate femoral tunnel placement was demonstrated both with respect to distance of the tunnel from Schöttle point and with regard to surgical time. Level of evidence was IV.
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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 | 0.003 | 0.029 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".