A Biomechanical and Finite Element Analysis of Femoral Neck Notching During Hip Resurfacing
Bibliographic record
Abstract
Hip resurfacing is an alternative to total hip arthroplasty in which the femoral head surface is replaced with a metallic shell, thus preserving most of the proximal femoral bone stock. Accidental notching of the femoral neck during the procedure may predispose it to fracture. We examined the effect of neck notching on the strength of the proximal femur. Six composite femurs were prepared without a superior femoral neck notch, six were prepared in an inferiorly translated position to create a 2 mm notch, and six were prepared with a 5 mm notch. Six intact synthetic femurs were also tested. The samples were loaded to failure axially. A finite element model of a composite femur with increasing superior notch depths computed maximum equivalent stress and strain distributions. Experimental results showed that resurfaced synthetic femurs were significantly weaker than intact femurs (mean failure of 7034 N, p<0.001). The 2 mm notched group (mean failure of 4034 N) was significantly weaker than the un-notched group (mean failure of 5302 N, p=0.018). The 5 mm notched group (mean failure of 2808 N) was also significantly weaker than both the un-notched and the 2 mm notched groups (p<0.001, p=0.023, respectively). The finite element model showed the maximum equivalent strain in the superior reamed cancellous bone increasing with corresponding notch size. Fracture patterns inferred from equivalent stress distributions were consistent with those obtained from mechanical testing. A superior notch of 2 mm weakened the proximal femur by 24%, and a 5 mm notch weakened it by 47%. The finite element analysis substantiates this showing increasing stress and strain distributions within the prepared femoral neck with increasing notch depth.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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 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".