The Effect of Cross-Sectional Stem Shape on the Torsional Stability of Cemented Implant Components
Bibliographic record
Abstract
Stability of a cemented implant, once the stem-cement interface has debonded, is reliant upon stem geometry and surface finish. There are relatively few studies addressing the effect of cross-sectional stem shape on cemented implant fixation. The purpose of this investigation was to compare the torsional stability of five different stem cross-sectional shapes-circular, oval, triangular, rectangular with rounded edges, and rectangular with sharp edges-under monotonically increasing and cyclic loading conditions. Seven samples of each stem geometry were tested. Stems were potted in bone cement and loaded to 5 deg of rotation. For monotonic loading, torque was applied at a constant rate of 2.5 deg/min. For cyclic loading, a sine wave torque pattern was applied, with a maximum magnitude that began at 4.5 Nm for 1500 cycles and then increased by 2.25 Nm every 1500 cycles until 5 deg of rotation. The rectangular stem with the sharp edges always provided the greatest resistance to torque, followed by the rectangular with rounded edges, triangular, oval, and circular. These results, including the effects of sharp corners, may differ for modes of loading other than torsion. These experimental results support the findings of earlier finite element models, indicating stem shape has a significant effect on resistance to torsional loading.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".