The Effect of Stem Curvature on Torsional Stability of a Generalized Cemented Joint Replacement System
Bibliographic record
Abstract
PURPOSE: Implant loosening is a common complication that compromises the stability of joint replacement systems. Stem geometry is particularly influential in the stability of cemented implants, both before and after debonding occurs at the stem-cement interface. There are few studies assessing the effect of stem longitudinal curvature as a geometric factor in cemented implant stability. The purpose of this study was to compare the torsional stability of four generalized cemented implant stems (i.e., non-specific to joint), with varying degrees of longitudinal curvatures--zero, two, four, and six degrees. METHODS: Twelve specimens of each curvature angle were potted to a depth of 20 mm using bone cement, given 24 hours to cure, and then tested in a materials testing machine. Torque was applied to the stems under monotonic loading at a rate of 2.5 degrees/min, until five degrees of rotation had occurred. RESULTS: There were no differences in torsional stability among the four stem curvature angles, when the magnitudes of peak torque (P=.72; 1-β = 0.13), rotation of the stem at peak torque (P=0.23; 1-β = 0.38) and work required for five degrees of stem rotation (P=.58; 1-β = 0.07) were compared. CONCLUSIONS: The findings from this study demonstrate that for short stems, stem curvature angles up to six degrees does not improve torsional stability when compared to the straight stem design.
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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 | 0.004 |
| 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 | 0.001 |
| 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".