Role of Current Proposed Algorithm to Guide Osteoporosis Treatment in CKD: A Bone Biopsy Study
Bibliographic record
Abstract
Background: Recently, algorithms have been proposed to guide osteoporosis treatment in chronic kidney disease population. As suggested by Kharaillah et al, evaluation of bone turnover level by bone specific phosphatase alkaline (bALP) will determine the use of anabolic or antiresorptive therapy with or without prior bone biopsy. The aim of this study is to use a cohort of CKD patients who had a bone biopsy to evaluate accuracy of this algorithm in a real-world setting. Methods: Single-center retrospective cross-sectional study at CHU de Québec, Canada from 2017 to 2021. CKD 4-5 patients with bone fragility and suspicion of low bone turnover or mineralization defects who had a bone biopsy were included. Results of bone biopsy were categorized based on the TMV classification. We compared the performance of the algorithm to identify potential contraindications to antiresorptive or anabolic therapy vs bone biopsy results. Receiver operating characteristic (ROC) curves were used to explore the predictive ability of bALP and tALP regarding low bone turnover and potential contraindication to antiresorptive therapy in our cohort. Results: Twenty-six patients included (mean age 67,7 years, 11 men, 14 HD and 1 PD, 11 diabetic patients). Eleven patients had low, 8 normal and 7 high bone turnover on biopsy. According to the algorithm, no patient would have received anabolic treatment, bone biopsy would have been proposed to 10 patients and 16 would have received antiresorptive therapy. Based on the biopsy results, 8 out of these 16 patients had potential contraindications: 4 with low bone turnover and 4 with presence of mineralization defects. ROC curve for bPAL to predict low bone turnover was 0,749 (similar to tPAL). However, the AUC for bPAL to predict the presence of potential contraindication to antiresorptive was lower at 0,6667 (0,6095 for tPAL). Conclusions: Algorithm using bone turnover markers can guide clinicians in approaching these patients. However, bone biopsy is still needed in many patients to better tailor anti fracture therapy until more accurate non-invasive markers are available. Funding: Private Foundation Support, Government Support - Non-U.S.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".