Effect of Teaching Session on Resident Ability to Identify Anatomic Landmarks and Anterior Cruciate Ligament Footprint: A Study Using 3-Dimensional Modeling
Bibliographic record
Abstract
Background: Femoral tunnel positioning in anterior cruciate ligament reconstruction (ACLR) is an intricate procedure that requires highly specific surgical skills. Purpose: To report the ability of residents to identify femoral landmarks and the native ACL footprint before and after a structured formal teaching session as a reflection of overall surgical skill training for orthopaedic surgery residents in Canada. Study Design: Controlled laboratory study. Methods: A total of 13 senior orthopaedic residents were asked to identify a femoral landmark and an ACL footprint on ten 3-dimensional (3D)–printed knee models before and after a teaching session during the fall of 2018. The 3D models were made based on actual patients with different anatomic morphologic features. ImageJ software was used to quantify the measurements, which were then analyzed through use of descriptive statistics. Results: Before and after the teaching session, residents attempted to identify a specific anatomic location (bifurcate and intercondylar ridge intersection) with a mean error per participant ranging from 5.00 to 10.95 mm and 4.79 to 12.13 mm in magnitude, respectively. Furthermore, before and after the teaching session, residents identified the specific position to perform the surgical procedure (ACL femoral footprint), with a mean error per participant ranging from 4.58 to 8.80 mm and 3.87 to 11.07 mm in magnitude, respectively. The teaching session resulted in no significant improvement in identification of either the intersection of the bifurcate and intercondylar ridges ( P = .9343 in the proximal-distal axis and P = .8133 in the anteroposterior axis) or the center of the femoral footprint ( P = .7761 in the proximal-distal axis and P = .9742 in the anteroposterior axis). Conclusion: Although a formal teaching session was combined with a hands-on session that entailed real surgical instrumentation and fresh cadaveric specimens, the intervention seemed to have no direct impact on senior residents’ performance or their ability to demonstrate the material taught. This puts into question the format and efficacy of present teaching methods. Also, it is possible that the 3D spatial perception required to perform these skills is not something that can be taught effectively through a teaching session or at all. Further investigation is required regarding the effectiveness and application of surgical skill laboratories and simulations on the competencies of orthopaedic residents.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".