Longitudinal HR-pQCT and Image Registration Detects Endocortical Bone Loss in Kidney Transplantation Patients
Bibliographic record
Abstract
Patients with chronic kidney disease (CKD) who undergo kidney transplantation experience bone loss and increased risk of fracture. However, the mechanisms of this bone loss are unclear. Our objective was to use image registration to define the cortex to assess changes in cortical porosity (Ct.Po) in patients undergoing first-time kidney transplantation. We obtained serial measurements of parathyroid hormone (PTH) and bone turnover markers and used high-resolution peripheral quantitative computed tomography (HR-pQCT) to scan the distal radius and tibia in 31 patients (21 men, 10 women; aged 51.9 ± 13.4 years) at transplant and after 1 year. Baseline and 1-year images were aligned using a fully automated, intensity-based image registration framework. We compared three methods to define the cortical region of interest (ROI) and quantify the changes: 1) cortical bone was independently defined in baseline and follow-up scans; 2) cortical bone was defined as the common cortical ROI; and 3) the cortical ROI at baseline was carried forward to 1-year follow-up (baseline-indexed). By the independently defined ROI, Ct.Po increased 11.7% at the radius and 9.1% at the tibia, whereas by the common ROI, Ct.Po increased 14.6% at the radius and 9.1% at the tibia. By the baseline-indexed ROI, which provides insight into changes at the endocortical region, Ct.Po increased 63.4% at the radius and 17.6% at the tibia. We found significant relationships between changes in Ct.Po and bone formation and resorption markers at the radius. The strongest associations were found between markers and Ct.Po using the baseline-index method. We conclude that Ct.Po increases throughout the cortex after kidney transplant, and this increase is particularly marked at the endocortical surface. These methods may prove useful for all HR-pQCT longitudinal studies, particularly when changes are expected at the endocortical region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".