Prevalence of incomplete atypical femoral fractures using single energy absorptiometry after long-term anti-resorptive therapy
Bibliographic record
Abstract
Atypical femur fractures (AFFs) have been reported with long-term use of anti-resorptive drugs. Early identification is crucial because it allows early intervention to stop the progression to complete fracture, thus potentially reducing the ensuing burden. It has been shown that extending the scan image to take a full-length image of the femur (FFI) using single energy (SE) X-rays at the time of a dual-energy X-ray absorptiometry (DXA) scan can detect findings in the spectrum of AFF. Following the International Society for Clinical Densitometry (ISCD) recommendations, FFI by SE X-ray is being performed for all patients who present to the Calcium Metabolism and Osteoporosis program at AUBMC for bone mineral density measurement by DXA, if they have received anti-resorptive drug for a cumulative period of 3 years or more. Patients can be currently on anti-resorptive drug or have discontinued it within the past 5 years prior to scan, instead of the 1 year, as recommended by the ISCD. The primary aim of this retrospective study was to assess the prevalence of findings in the spectrum of AFF using FFI by SE X-rays. We collected data on demographic factors, clinical risk factors for osteoporosis, and bone densitometry parameters. Out of the 948 patients, 18 patients were found to have findings in the spectrum of AFF; 14 underwent subsequent imaging studies to investigate and confirm these abnormalities. One patient out of 948 patients was found to have an incomplete AFF confirmed by computed tomography scan. Studying the prevalence of the signs of AFF on FFI in other studies and assessing the specificity of this technique by comparing its findings with more established methods is important. Future ISCD task forces may need to reassess efficacy and cost effectiveness of its recommended guidance on using SE femur in patients to prevent adverse 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".