Epidemiology of periprosthetic fracture of the femur in 32 644 primary total hip arthroplasties
Bibliographic record
Abstract
AIM AND METHODS: The goals of this study were to define the risk factors, nature, chronology, and treatment strategies adopted for periprosthetic femoral fractures in 32 644 primary total hip arthroplasties (THAs). RESULTS: There were 564 intra-operative fractures (1.7%); 529 during uncemented stem placement (3.0%) and 35 during cemented stem placement (0.23%). Intra-operative fractures were more common in females and patients over 65 years (p < 0.001). The majority occurred during placement of the femoral component (60%), and involved the calcar (69%). There were 557 post-operative fractures (20-year probability: 3.5%; 95% confidence interval (CI) 3.2 to 3.9); 335 fractures after placement of an uncemented stem (20-year probability: 7.7%; 95% CI 6.2 to 9.1) and 222 after placement of a cemented stem (20-year probability: 2.1%; 95% CI 1.8 to 2.5). The probability of a post-operative fracture within 30 days after an uncemented stem was ten times higher than a cemented stem. The most common post-operative fracture type was a Vancouver AG (32%; n = 135), with 67% occurring after a fall. In all, 36% (n = 152) were treated with revision arthroplasty. CONCLUSION: In summary, intra-operative fractures occur 14 times more often with uncemented stems. Female patients over 65 years of age are at highest risk. Post-operative fractures are also most common with uncemented stems, but are independent of age or gender. Cumulative risk of post-operative periprosthetic femur fracture was 3.5% at 20 years. TAKE HOME MESSAGE: Intra-operative fractures occur 14 times more often with uncemented stems, particularly with female patients over 65 years of age, while post-operative fracture risk is independent of age or gender, but still increased with uncemented stems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".