FRAX Predicts Fracture Risk in Kidney Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: The World Health Organization Fracture Risk Assessment Tool (FRAX) estimates the 10-year fracture probability. We assessed the prognostic value of FRAX in kidney transplant recipients, as its utility in recipients is unknown. METHODS: We considered 458 individuals (mean age 45 years, 64% men) who received a kidney transplant in the province of Manitoba, Canada at the time of their first bone mineral density (BMD) test posttransplant (mean 1.1 years posttransplant; transplant years 1996-2011). FRAX probabilities were calculated from baseline information (age, sex, clinical risk factors, with or without BMD). Recipients were followed a mean of 6.4 years (interquartile range 3.0-10.0 years) after cohort entry for an incident major osteoporotic fracture. RESULTS: In follow-up, 21 (4.6%) recipients experienced a major osteoporotic fracture. The observed 10-year major osteoporotic fracture risk of 6.3% (95% CI, 3.4-9.2%) was concordant with FRAX predictions (5.0% with BMD, 5.6% without BMD). Major osteoporotic fracture scores showed significant fracture prediction (hazard ratio per standard deviation, FRAX without BMD 1.66, 95% CI, 1.10-2.50; FRAX with BMD 1.64, 95% CI, 1.07-2.51). Area under the curve (AUC) for incident major osteoporotic fracture discrimination (AUC: FRAX with BMD 0.62, 95% CI, 0.50-0.74) was similar to the general population. CONCLUSIONS: FRAX scores categorized most kidney transplant recipients as a low-risk fracture group, and the low observed fracture rates were consistent with the 10-year fracture predictions. FRAX showed modest fracture prediction and discrimination similar to the general population. Independent validation is needed before clinicians can routinely use FRAX in kidney transplant recipients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".