Evaluating femoral augmentation to prevent geriatric hip fracture: A scoping review of experimental methods
Bibliographic record
Abstract
Various femoral augmentation designs have been investigated over the past decade for the prevention of geriatric hip fracture. The experimental methods used to evaluate the efficacy of these augmentations have not been critically evaluated or compared in terms of biofidelity, robustness, or ease of application. Such parameters have significant relevance in characterizing future clinical success. In this study we aimed to use a scoping review to summarize the experimental studies that evaluate femoral augmentation approaches, and critically evaluate commonly applied protocols and identify areas for concordance with the clinical situation. We conducted a literature search targeting studies that used experimental test methods to evaluate femoral augmentation to prevent geriatric fragility fracture. A total of 25 studies met the eligibility criteria. The most commonly investigated augmentation to date is the injection of bone cement or another material that cured in situ, and a popular subsequent method for biomechanical evaluation was to load the augmented proximal femur until fracture in a sideways fall configuration. We noted limitations in the clinical relevance of sideways fall scenarios being modeled and large variance in the concordance of many of the studies identified. Our review brings about recommendations for enhancing the fidelity of experimental methods modeling clinical sideways falls, which include an improved representation of soft tissue effects, using outcome metrics beyond load-to-failure, and applying loads inertially. Effective augmentations are encouraging for their potential to reduce the burden of hip fracture; however, the likelihood of this success is only as strong as the methods used in their evaluation.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".