Short stems have lower load at failure than double-wedged stems in a cadaveric cementless fracture model
Bibliographic record
Abstract
Objectives Periprosthetic femoral fractures (PFFs) have a higher incidence with cementless stems. The highest incidence among various cementless stem types was observed with double-wedged stems. Short stems have been introduced as a bone-preserving alternative with a higher incidence of PFF in some studies. The purpose of this study was a direct load-to-failure comparison of a double-wedged cementless stem and a short cementless stem in a cadaveric fracture model. Methods Eight hips from four human cadaveric specimens (age mean 76 years (60 to 89)) and eight fourth-generation composite femurs were used. None of the cadaveric specimens had compromised quality (mean T value 0.4 (-1.0 to 5.7)). Each specimen from a pair randomly received either a double-wedged stem or a short stem. A materials testing machine was used for lateral load-to-failure test of up to a maximal load of 5000 N. Results Mean load at failure of the double-wedged stem was 2540 N (1845 to 2995) and 1867 N (1135 to 2345) for the short stem (p < 0.001). All specimens showed the same fracture pattern, consistent with a Vancouver B2 fracture. The double-wedged stem was able to sustain a higher load than its short-stemmed counterpart in all cases. Failure force was not correlated to the bone mineral density (p = 0.718). Conclusion Short stems have a significantly lower primary load at failure compared with double-wedged stems in both cadaveric and composite specimens. Surgeons should consider this biomechanical property when deciding on the use of short femoral stem. Cite this article: A. Klasan, M. Bäumlein, P. Dworschak, C. Bliemel, T. Neri, M. D. Schofer, T. J. Heyse. Short stems have lower load at failure than double-wedged stems in a cadaveric cementless fracture model. Bone Joint Res 2019;8:489–494. DOI: 10.1302/2046-3758.810.BJR-2019-0051.R1.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".