Bibliographic record
Abstract
Periprosthetic fractures of the hip are serious complications following total hip arthroplasty, particularly affecting elderly patients, those with osteoporosis, or individuals with a history of revision surgeries. These fractures may occur intraoperatively or postoperatively, involving the femoral shaft (classified as Vancouver A, B, or C) or the acetabulum (Paprosky classification). Acetabular fractures are less common than femoral fractures. The mechanisms of fracture involve both biological factors (such as stress risers, implant loosening, and poor bone quality) and external forces (such as falls, torsional, and bending loads). Identified risk factors include advanced age, female gender, osteoporosis, implant loosening, use of cementless femoral stems, rheumatoid arthritis, and multiple revision surgeries. Studies have reported the incidence of periprosthetic fractures after primary hip replacement ranging from 0.8% to 4.5%, with higher rates observed following revision procedures. Diagnosis relies on both clinical and paraclinical assessments, with plain radiographs and computed tomography (CT) being the primary tools to evaluate implant stability, bone loss, and to guide treatment planning. Treatment depends on the type of fracture and the stability of the prosthesis. Stable Vancouver A fractures may be managed conservatively; Vancouver B1 fractures often require internal fixation with plates and screws; B2 and B3 fractures typically necessitate revision with a long-stem prosthesis and bone grafting if needed. Nondisplaced acetabular fissure fractures may be treated conservatively, but displaced or unstable fractures require surgical reconstruction using plates, screws, bone grafts, or specialized acetabular components. Prognosis is influenced by patient age, the severity of osteoporosis, prosthesis stability, and the number of prior surgeries. The mortality rate following periprosthetic hip fractures ranges from 7% to 18% within the first year postoperatively. Prevention, thorough risk assessment, and appropriate treatment strategies play crucial roles in improving clinical outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.129 | 0.026 |
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".