High-speed x-ray characterizes fracture incidence and bone-implant motion during a fall from standing
Bibliographic record
Abstract
BACKGROUND: Fall-related traumas like hip fracture are a common yet devastating injury with poor outcomes. Characterizing fracture biomechanics and bone-implant kinematics is essential to increase our understanding of these events to inform treatment and prevention strategies. METHODS: This study developed a bilateral high-speed x-ray methodology for the real-time capture of fracture and kinematic data near the hip during fall impacts. High speed x-ray was applied to capture fall impacts of seven cadaveric pelvis-femur specimens encased in a soft tissue surrogate, using a previously developed method. In these specimens, the intact proximal femur had been prophylactically reinforced with an intramedullary nailing system intended to prevent fragility fractures. The feasibility of extracting 3D kinematic data from x-ray data was investigated. FINDINGS: The HSXR system demonstrated visual clarity and sufficient resolution for capturing skeletal fracture and kinematics. The data in this study revealed fracture and newly-seen deformations of the pelvis, highlighting the ability of the x-ray system to document real-time fracture and kinematic events. Kinematic data in 3D was extracted with sufficient accuracy for one specimen. INTERPRETATION: These results demonstrate the merit of high-speed x-ray for studying periprosthetic fracture, which is of increasing relevance due to increasing populations with orthopedic hardware. Application of this method advances our understanding of impact-related biomechanics and fracture mechanics during a clinically-relevant fall from standing.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".