Bone imaging modality precision and agreement between DXA, pQCT, and HR-pQCT
Bibliographic record
Abstract
Abstract Quantifying precision error for DXA, peripheral QCT (pQCT), and HR-pQCT is crucial for monitoring longitudinal changes in body composition and musculoskeletal outcomes. Agreement and associations between bone variables assessed using pQCT and second-generation HR-pQCT are unclear. This study aimed to determine the precision of, and agreement and associations between, bone variables assessed via DXA, pQCT, and second-generation HR-pQCT. Thirty older adults (mean age: 64.2 ± 8.0 yr; women: 67%) were recruited. DXA scans were performed at the total hip, lumbar spine, and whole body. Distal (4%) and proximal (30%/33%/66%) skeletal sites at the radius and tibia were scanned with pQCT and/or HR-pQCT. Root-mean-squared coefficients of variation (%CVRMS) were calculated to define precision errors, and Bland–Altman plots assessed agreement between densitometric estimates. Pearson correlations and linear regression explored relationships between bone variables at different skeletal sites and proportional bias, respectively. Precision errors ranged between 0.55% and 1.6% for DXA, 0.40% and 4.8% for pQCT, and 0.13% and 30.7% for HR-pQCT. Systematic bias was identified between pQCT- and HR-pQCT-determined radius and tibia volumetric BMD (vBMD) estimates (all p<.001). Proportional bias was not observed between vBMD measures at any skeletal site (all p>.05). pQCT- and HR-pQCT-determined total, trabecular, and cortical vBMD and estimates of bone strength at the radius and tibia were strongly correlated (all p<.05). Precision error was low for most bone variables and within the expected range for all imaging modalities. We observed significant systematic bias, but no proportional bias, between pQCT- and second-generation HR-pQCT-determined vBMD estimates at the radius and tibia. Nevertheless, measures of bone density and strength were strongly correlated at all skeletal sites. These findings suggest that although bone density and strength estimates from both imaging modalities are not interchangeable, they are strongly related and likely have similar fracture prediction capabilities.
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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 | 0.033 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".