Improved reproducibility of high-resolution peripheral quantitative computed tomography for measurement of bone quality
Bibliographic record
Abstract
A human high-resolution peripheral quantitative computed tomography scanner (HR-pQCT) (XtremeCT, Scanco Medical, Switzerland) capable of measuring three important indicators of bone quality (micro-architectural morphology, mineralization and mechanical stiffness) has been developed. The goal of this study was to evaluate the reproducibility of male and female HR-pQCT in vivo measurements, and elucidate the causes of error in these measurements through a comparison with in vitro measurements. The best possible short-term reproducibility was found using a set of 10 in vitro measurements without repositioning, and a set of 10 with repositioning. Subsequently, in vivo measurements were performed on 15 male and 15 female subjects at baseline and follow-ups of 1 week and 4 months to determine the short- and long-term reproducibility of the system. In addition to the 2D area matching method used in the standard evaluation protocol, a custom developed 3D registration method was used to find the common region between repeated scans. The best possible reproducibility without movement artifacts and repositioning error was less than 0.5%, while the reproducibility with repositioning error was less than 1.5%. The in vivo reproducibility of density (<1%), morphological (<4.5%) and stiffness (<3.5) measurements was consistently poorer than the reproducibility of cadaver measurements, presumably due to small movement artifacts and repositioning errors. Using 3D image registration, repositioning error was reduced on average by 23% and 8% for measurements of the radius and tibia sites, respectively. This study has provided bounds for the reproducibility of HR-pQCT to monitor bone quality longitudinally, and a basis for clinical study design to determine detectable changes.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".