Clinical characteristics and risk factors of periprosthetic femoral fractures associated with hip arthroplasty
Bibliographic record
Abstract
Periprosthetic femoral fracture (PFF) is a complicated complication of both primary and revision hip arthroplasty with an increasing incidence. The present study aimed to summarize the clinical characteristics and identify the risk factors for PFF which would be potentially helpful in the prevention and treatment of PFF.We retrospectively analyzed the clinical data of 89 cases of PFF, and a case-control study was designed to identify the potential risk for intraoperative and postoperative PFF in both primary and revision hip arthroplasty.The overall incidence of PFF was 2.08% (intraoperative: 1.77%, postoperative: 0.30%, revision: 13.60%, and primary: 0.97%). The most commonly used treatment strategy was fixation with cerclage wire or band for intraoperative PFF, whereas long stem revision with plate or cortical allograft strut fixation was the main treatment strategy for postoperative PFF. The risk factors for intraoperative PFF in primary total hip arthroplasty (THA) included the diagnosis of development dysplasia of the hip (DDH) (odds ratio [OR] = 5.01, 95%CI, 1.218-20.563, P=0.03) and CBR ≥ 0.49 (OR = 3.34, 95%CI, 1.138-9.784, P = 0.03). The increased age was associated with increased incidence of postoperative PFF in primary THA (OR = 1.09, 95%CI, 1.001-1.194, P = 0.04). As for the intraoperative PFF in revision THA, we found that receiving multiple operations before revision (OR = 2.45, 95%CI, 1.06-5.66, P = 0.04), revisions due to prosthetic joint infection (OR = 6.72, 95%CI, 1.007-44.832, P = 0.04), the presence of cementless implant before revision (OR = 13.54, 95%CI, 3.103-59.08, P = 0.001), and femoral deformity (OR = 8.03, 95%CI, 1.656-38.966, P = 0.01) were all risk factors.Screening for high-risk patients, preoperative templating, and detailed discharge instructions may be the potential strategies to reduce the incidence of PFF. The treatment of PFFs should take into account Vancouver classification system, patient's characteristics as well as the experience of the operating surgeon.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 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".