The reproducibility of measuring trabecular bone parameters using a commercially available high-resolution magnetic resonance imaging approach: A pilot study
Bibliographic record
Abstract
Bone imaging is currently the best non-invasive way to assess changes to bone associated with aging or chronic disease. However, common imaging techniques such as dual energy x-ray absorptiometry are associated with limitations. Magnetic resonance imaging (MRI) is a radiation-free technique that can measure bone microarchitecture. However, published MRI bone assessment protocols use specialized MRI coils and sequences and therefore have limited transferability across institutions. We developed a protocol on a Siemens 3 Tesla MRI machine, using a commercially available coil (Siemens 15 CH knee coil), and manufacturer supplied sequences to acquire images at the tibia. We tested the reproducibility of the FSE and the GE Axial sequences and hypothesized that both would generate reproducible trabecular bone parameters. Eight healthy adults (age 25.5 ± 5.4 years) completed three measurements of each MRI sequence at the tibia. Each of the images was processed for 8 different bone parameters (such as volumetric bone volume fraction). We computed the coefficient of variation (CV) and intraclass correlation coefficients (ICC) to assess reproducibility and reliability. Both sequences resulted in trabecular parameters that were reproducible (CV <5% for most) and reliable (ICC >80% for all). Our study is one of the first to report that a commercially available MRI protocol can result in reproducible data, and is significant as MRI may be an accessible method to measure bone microarchitecture in clinical or research environments. This technique requires further testing, including validation and evaluation in other populations.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".