Timing of femoral prosthesis insertion during cemented arthroplasty: cement curing and static mechanical strength in an in vivo model
Bibliographic record
Abstract
BACKGROUND: Modern cementing techniques aim to fix the implanted femoral prosthesis in the medullary cavity to minimize long-term complications such as aseptic loosening. The cure stage of bone cement into which the femoral component is being inserted is an important variable that is decided at the time of surgery. Late-cure cement is more viscous than early-cure cement and requires greater force on the part of the surgeon to insert the femoral prosthesis. We compared 2 cementing techniques, femoral component insertion into early-cure cement and insertion into late-cure cement, using an in vivo model to identify if cement cure stage affects the strength of the bone-cement interface. METHODS: We performed bilateral hemiarthroplasties using only the femoral component in vivo on paired porcine femora. The femora were harvested and cross-sectioned in preparation for strength testing. We measured bond strength by peak load required to push the femoral prosthesis and surrounding cement mantle free of the cancellous bone. RESULTS: All radiographs showed good cement interdigitation with no evidence of radiolucent lines at the bone-cement interface. We could not differentiate the early-cure and late-cure groups on postoperative radiographs. The mean failure load for the late-cure arthroplasties was 908 N (standard deviation [SD] 420 N), whereas the mean failure load for the conjugate early-cure arthroplasties was 503 N (SD 342 N). CONCLUSION: Femoral component insertion into late-cure cement required significantly higher loads for push-out than femoral component insertion into early-cure cement.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".