Use of dual energy X‐ray absorptiometry, the trabecular bone score and quantitative computed tomography in the evaluation of chronic kidney disease‐mineral and bone disorders
Bibliographic record
Abstract
In subjects with chronic kidney disease (CKD) who suffer a minimal trauma fracture, the problem is to differentiate between osteoporosis and the various forms of renal bone disease associated with CKD-mineral and bone disorder. This problem is exacerbated by the fact that renal osteodystrophy may coexist with osteoporosis. The World Health Organization's bone mineral density (BMD) criteria for osteopenia ( -2.5 < T-score < -1.0) and osteoporosis (a T-score ≤ -2.5) may be used in patients with CKD stages 1-3. In CKD stages 4-5, BMD by dual-energy X-ray absorptiometry (DXA) is less predictive and may underestimate fracture risk. The development of absolute fracture risk (AFR) algorithms, such as FRAX® and the Garvan absolute fracture risk calculator, to predict risk of fracture over a given time (usually 10 years) aims to incorporate non-BMD risk factors into the clinical assessment. FRAX® has been shown to be useful to assess fracture risk in CKD but may underestimate fracture risk in advanced CKD. The trabecular bone score is a measure of grey scale homogeneity obtained from spine DXA, which correlates to trabecular microarchitecture and is an independent risk factor for fracture. Recent data demonstrate the potential utility of the trabecular bone score adjustment of AFR through the FRAX® algorithm in subjects with CKD. Parameters of bone microarchitecture using peripheral quantitative computed tomography (pQCT) or high-resolution pQCT are also able to discriminate fracture status in subjects with CKD. However, there are at present no convincing data that the addition of pQCT or high-resolution pQCT parameters to DXA BMD improves fracture discrimination. More advanced estimates of bone strength derived from measurements of micro-architecture, by QCT-derived finite element analysis may be incorporated into AFR algorithms in the future.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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".