Bone histomorphometry for the diagnosis of renal osteodystrophy – a European consensus statement
Bibliographic record
Abstract
Histomorphometric analysis of an iliac bone biopsy remains the gold standard for the diagnosis of renal osteodystrophy (ROD), which comprises various histological lesions induced by chronic kidney disease (CKD). ROD belongs to the framework of CKD-associated osteoporosis. The use of bone biopsy in the routine management of CKD-associated osteoporosis has decreased over the past decades for various reasons, including diminishing expertise in performing the procedure, and major variability in processing bone samples and reporting of results. In this context, the European Renal Osteodystrophy group, a part of the CKD-mineral and bone disorder working group of the European Renal Association launched an initiative to evaluate various issues related to bone histomorphometry in the context of ROD. To this effect, 28 experts from 14 European countries engaged in rounds of discussions to reach a consensus related to the bone biopsy procedure, sample handling, and reading and reporting findings. Key conclusions include a recommendation that all practitioners in this field move towards reporting diagnostic findings by the turnover, mineralization, and volume (TMV) classification and that external quality control is prioritized to ensure validity and reproducibility of results. The consensus group recognises that the lack of an accepted normative reference for bone histomorphometry is a barrier towards uniform diagnostic definitions and recommends further collaborative efforts in this area. Until these issues are solved, transparent reporting on the choice of reference and diagnostic definitions applied should be adhered to, both in clinical reports and research settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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".