MP389ASSOCIATION OF BONE MINERAL DENSITY WITH FRACTURES ACROSS THE SPECTRUM OF CHRONIC KIDNEY DISEASE: THE PRAIRIE DXA STUDY
Bibliographic record
Abstract
Introduction and Aims: The value of Dual energy X ray Absorptiometry (DXA) scans in postmenopausal women in predicting fractures is robust. However, its role in patients with chronic kidney disease (CKD) stages III-V is controversial. Methods: 410 consecutive patients who underwent DXA scan at the point of entry into our multidisciplinary CKD program were included. Bone mineral density (BMD) data, T score and Z scores were collected at four sites: the lumbar spine, total hip, mean of left and right femoral neck, and the proximal radial region (Radius 33%). We collected data on demographic, lab markers of mineral metabolism and fractures (identified through self-reported questionnaires, hospital electronic medical records and physician billing records). Results: 35.9% stage III CKD, 28.4 % stage IV CKD, and 32.4% stage V CKD experienced a clinical fracture during the study period. On multivariate analysis, we observed a decline of 1.0 SD in T-score is associated with a statistically significant increase in the risk of fracture after addition of biochemical parameters such as Parathyroid hormone (PTH), Alkaline Phosphatase (ALP), calcium and phosphorus and estimated- Glomerular filtration rate (e-GFR) of <30 (OR= 1.36, 95% CI: 1.02, 1.72). In patients with a GFR of ≥30 mls/ minute, the odds ratio (OR) of identifying a fracture was 1.54 in comparison to OR of 1.14 in patients with GFR <29 mls/minute.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".