Effect of surgical experience on imageless computer-assisted femoral component positioning in hip resurfacing – a preclinical study
Bibliographic record
Abstract
BACKGROUND: The clinical outcome of hip resurfacing (HR) as a demanding surgical technique associated with a substantial learning curve depends on the position of the femoral component. The aim of the study was to investigate the effects of the level of surgical experience on computer-assisted imageless navigation concerning precision of femoral component positioning, notching, and oversizing rate, as well as operative time. METHODS: Three surgeons with different levels of experience in both HR and computer-assisted surgery (CAS) prepared the femoral heads of 54 synthetic femurs using the Durom(TM) Hip Resurfacing (Zimmer, Warsaw, IN, USA) system. Each surgeon prepared a total of 18 proximal femurs using the Navitrack® system (ORTHOsoft Inc., Montreal, Canada) or the conventional free-hand Durom(TM) K-wire positioning jig. The differences between planned and postoperative stem shaft angle (SSA) and anteversion angle in standardized x-rays were measured and the operative time, not including the time for calibrating the CAS-system, was documented. Notching was evaluated by the three surgeons in a randomized manner. Oversizing was determined by the difference of the preoperative determined cap and the cap size advised by the CAS-system. RESULTS: CAS significantly reduced the overall mean deviation between planned and postoperative SSA in comparison with the conventional procedure (mean ± SD, 1 ± 1.7° vs. 7.4 ± 4.4°, P < 0.01) regardless of the surgeon's level of experience. The incidence of either varus or valgus SSA deviations exceeding 5° were 1/27 for CAS and 15/27 for the conventional method, respectively (P < 0.001), corresponding to a reduction by 97%. Using CAS, the rate of notching was reduced by 100%. CONCLUSIONS: The accuracy of femoral HR component orientation is significantly increased by use of CAS regardless of the surgeon's level of experience in our preclinical study. Thus, imageless computer-assisted navigation can be a valuable tool to improve implant positioning in HR for surgeons at any stage of their learning curve.
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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 | 0.034 | 0.003 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".