The Estimation of Second-Generation HR-pQCT From First-Generation HR-pQCT Using In Vivo Cross-Calibration
Bibliographic record
Abstract
ABSTRACT Second-generation high-resolution peripheral quantitative computed tomography (HR-pQCT) provides the highest resolution in vivo to assess bone density and microarchitecture in 3D. Although strong agreement of most outcomes measured with first- (XCTI) and second- (XCTII) generation HR-pQCT has been demonstrated, the ability to use the two systems interchangeably is unknown. From in vivo measurements, we determined the limits of estimating XCTII data from XCTI scans conducted in vivo and whether that estimation can be improved by linear cross-calibration equations. These data are crucial as the research field transitions to the new technology. Our study design established cross-calibration equations by scanning 62 individuals on both systems on the same day and then tested those cross-calibrations on the same cohort 6 months later so that estimated (denoted as XCTII*) and “true” XCTII parameters could be compared. We calculated the generalized least-significant change (GLSC) for those predictions. There was strong agreement between both systems for density (R2 > 0.94), macroarchitecture (R2 > 0.95), and most microarchitecture outcomes with the exception of trabecular thickness (Tb.Th, R2 = 0.51 to 0.67). Linear regression equations largely eliminated the systematic error between XCTII and XCTII* and produced a good estimation of most outcomes, with individual error estimates between 0.2% and 3.4%, with the exception of Tt.BMD. Between-system GLSC was similar to within-XCTI LSC (eg, 8.3 to 41.9 mg HA/cm3 for density outcomes). We found that differences between outcomes assessed with XCTI and XCTII can be largely eliminated by cross-calibration. Tb.Th is poorly estimated because it is measured more accurately by XCTII than XCTI. It may be possible to use cross-calibration for most outcomes when both scanner generations are used for multicenter and longitudinal studies. © 2017 American Society for Bone and Mineral Research.
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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 | 0.012 | 0.030 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".