The effect of femur positioning on dual-energy X-ray absorptiometry (DXA) measures and statistical shape and appearance modeling (SSAM) fracture risk assessments
Bibliographic record
Abstract
The diagnosis of osteoporosis using Dual-energy X-ray Absorptiometry (DXA) relies on accurate hip scans, whereby variability in measurements may be introduced by altered patient positioning, as could occur with repeated scans over time. The goal herein was to test how altered postures affect diagnostic metrics (i.e., standard clinical metrics and a newer image processing tool) for femur positioning. A device was built to support cadaveric femurs and adjust their orientation in 3° increments in flexion and internal/external rotation. Seven isolated femurs were scanned in six flexion postures (0° (neutral) to 15° of flexion) and eleven rotational postures (15° external to 15° internal rotation) while collecting standard clinical DXA-based measures for each scan. The fracture risk tool was applied to each scan to calculate fracture risk. Two separate one-way repeated measures ANOVAs (α = 0.05) were performed on the DXA-based measures and fracture risk prediction output. Flexion had a significant effect on T-score, Bone Mineral Density (BMD), and Bone Mineral Content (BMC), but not area, at angles greater than 12°. Internal and external rotation did not have a significant effect on any clinical metric. Fracture risk (as assessed by the image processing tool) was not affected by either rotation mode. Overall, this suggests clinicians can adjust patient posture to accommodate discomfort if deviations are less than 12 degrees, and the greatest care should be taken in flexion. Furthermore, the tool is relatively insensitive to postural adjustments, and as such may be a good option for tracking risk over repeated patient scans.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".