Training Femoral Neck Screw Insertion Skills to Surgical Trainees
Bibliographic record
Abstract
BACKGROUND: : Femoral neck fractures are among the most common orthopaedic injuries impacting the health care system. Surgical management of such fractures with cannulated screws is a commonly performed procedure. The acquisition of surgical skills necessary to perform this procedure typically involves learning on real patients with fluoroscopic guidance. This study attempts to determine if a novel computer-navigated training model improves the learning of this basic surgical skill. METHODS: A multicenter, prospective, randomized, and controlled study was conducted using surgical trainees with no prior experience in surgically managing femoral neck fractures. After a training session, participants underwent a pretest by performing the surgical task (screw placement) on a simulated hip fracture using fluoroscopic guidance. Immediately after, participants were randomized into either undergoing a training session using conventional fluoroscopy or computer-based navigation. Immediate posttest, retention (4 weeks later), and transfer tests were performed. Performance during the tests was determined by radiographic analysis of hardware placement. RESULTS: Screw placement by trainees was ultimately equal to the level of an expert surgeon with either training technique. Participants who trained with computer navigation took fewer attempts to position hardware and used less fluoroscopy time than those trained with fluoroscopy. When those trained with fluoroscopy used computer navigation at the transfer test, less fluoroscopy time and dosage was used. The concurrent augmented feedback provided by computer navigation did not affect the learning of this basic surgical skill in surgical novices. No compromise in learning occurred if the surgical novice trained with one type of technology and transferred to using the other. CONCLUSIONS: The findings of this study suggest that computer navigation may be safely used to train surgical novices in a basic procedure. This model avoids using both live patients and harmful radiation without a compromise in the acquisition of a 3-dimensional technical skill.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".