Periprosthetic Atypical Femoral Fractures in Patients on Long-term Bisphosphonates
Bibliographic record
Abstract
OBJECTIVES: To define the characteristics of periprosthetic atypical femoral fractures (PAFFs) in patients on long-term bisphosphonate treatment and to provide a guide to the diagnosis and long-term treatment of these patients based on the literature. DESIGN: Multicenter retrospective review. SETTING: Fifteen orthopaedic centers in the United States and Canada, including members of the Canadian Orthopaedic Trauma Society. PATIENTS/PARTICIPANTS: Patients on long-term bisphosphonates who presented with either periprosthetic fractures or femoral fractures, over a 10-year period. MAIN OUTCOME MEASUREMENTS: Time to union and complications. RESULTS: Clinically significant differences were identified in time to union, mortality, and complications. There was a statistically significant difference in complications. Imaging review demonstrated identical features in both atypical femoral fractures (AFFs) and PAFFs. CONCLUSIONS: This is the largest comparative case series reported on PAFFS and AFFs and provides compelling evidence that PAFFs in patients on long-term bisphosphonates are indeed a subset of periprosthetic fractures that exhibit atypical femoral fracture (AFF) characteristics. As such, these fractures pose serious diagnostic and management challenges to trauma and arthroplasty surgeons. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".