Iliac crest bone biopsy by interventional radiologists to improve access to bone biopsy in chronic kidney disease populations: technical note and a case series
Bibliographic record
Abstract
INTRODUCTION: Chronic kidney disease-mineral and bone disorder (CKD-MBD) leads to increased fracture risk. Iliac crest biopsy remains the gold standard for diagnosing bone disease in CKD. Unfortunately, bone biopsy is rarely performed which is mainly due to the inability of clinicians to perform the procedure. In this paper, we propose a fluoroscopy-guided procedure performed by interventional radiologists as a novel approach to iliac crest biopsy in adult population. We describe the implementation of the procedure and present the first 11 cases of CKD patients who underwent iliac crest biopsy with this new approach. METHODS: A nephrologist already trained in performing iliac crest biopsy initiated the creation of a fluoroscopy-based iliac crest biopsy program. Two interventional radiologists underwent a short training. Patients' demographical, clinical and biochemical data were collected on the day of the biopsy. Complications within the first three months after the procedure were collected from electronical records. RESULTS: IR rapidly mastered the procedure. The use of fluoroscopy allowed a precise localisation of the biopsy site and standardization of the intervention, which ensured specimen quality. The new approach allowed CKD patients to access iliac crest biopsy, which resulted in precise bone disease diagnosis (levels of bone turnover and mineralization) and targeted therapy for each case. There were no complications during, nor within 3 months after the intervention. CONCLUSIONS: We believe this approach will increase the access to iliac crest biopsy for diagnosing bone disease in CKD population. Studies are now needed to evaluate whether CKD patients will benefit from anti-osteoporotic therapy based on the results of iliac crest biopsy.
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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 | 0.000 | 0.001 |
| 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".