Risk factors affecting the incidence of postoperative periprosthetic femoral fracture in primary hip arthroplasty patients: a retrospective study.
Bibliographic record
Abstract
The purpose of this study was to identify the characteristics and risk factors for postoperative periprosthetic femoral fracture (PFF). This was a retrospective cohort study of 108 patients with and 432 control patients without postoperative PFF. Demographic characteristics, surgery-related information (primary hip disease diagnosed, fixation, femoral stem, method of operation, and bone resorption of the proximal femur), and postoperative patient outcomes (hip function, treatment history, and patients' lifestyle behaviors) were recorded and compared between the groups. PFF characteristics, such as the classification, time, and cause, were also documented, and a Cox regression model was built to identify the independent risk factors for postoperative PFF in these patients. Six independent risk factors for postoperative PFF were identified, namely, advanced age (hazard ratio (HR) = 1.026, 95% confidence interval (CI) = 1.007-1.045), femoral neck fracture as the primary disease (HR = 4.536, 95% CI = 2.955-6.961), osteoporosis (HR = 2.043, 95% CI = 1.234-3.383), hemiarthroplasty (or HA, HR = 2.173, 95% CI = 1.327-3.558), bone resorption of the proximal femur (HR = 1.627, 95% CI = 1.090-2.430), and a standard- or long-stem femoral prosthesis (HR = 2.996, 95% CI = 1.480-6.067). The predictive values for a low risk (estimated incidence ≤ 50%), moderate risk (estimated incidence 51%-89%), and high risk (estimated incidence ≥ 90%) of PFF were ≤ 3.0 points, 3.0-10.0 points, and ≥ 10.0 points, respectively. Most patients with postoperative PFF had Vancouver type B fractures. Six independent risk factors for postoperative PFF were identified: advanced age, hip fracture as the primary disease, osteoporosis, HA, bone resorption of the proximal femur, and a long femoral stem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".