Comparison of Fracture Risk Prediction among Individuals with Reduced and Normal Kidney Function
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The Fracture Risk Assessment Tool (FRAX) is widely used to predict the 10-year probability of fracture; however, the clinical utility of FRAX in CKD is unknown. This study assessed the predictive ability of FRAX in individuals with reduced kidney function compared with individuals with normal kidney function. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The discrimination and calibration (defined as the agreement between observed and predicted values) of FRAX were examined using data from the Canadian Multicentre Osteoporosis Study (CaMos). This study included individuals aged ≥40 years with an eGFR value at year 10 of CaMos (defined as baseline). The cohort was stratified by kidney function at baseline (eGFR<60 ml/min per 1.73 m(2) [72.2% stage 3a, 23.8% stage 3b, and 4.0% stage 4/5] versus ≥60 ml/min per 1.73 m(2)) and followed individuals for a mean of 4.8 years for an incident major osteoporotic fracture (clinical spine, hip, forearm/wrist, or humerus). RESULTS: There were 320 individuals with an eGFR<60 ml/min per 1.73 m(2) and 1787 with an eGFR≥60 ml/min per 1.73 m(2). The mean age was 67±10 years and 71% were women. The 5-year observed major osteoporotic fracture risk was 5.3% (95% confidence interval [95% CI], 3.3% to 8.6%) in individuals with an eGFR<60 ml/min per 1.73 m(2), which was comparable to the FRAX-predicted fracture risk (6.4% with bone mineral density; 8.2% without bone mineral density). A statistically significant difference was not observed in the area under the curve values for FRAX in individuals with an eGFR<60 ml/min per 1.73 m(2) versus ≥60 ml/min per 1.73 m(2) (0.69 [95% CI, 0.54 to 0.83] versus 0.76 [95% CI, 0.70 to 0.82]; P=0.38). CONCLUSIONS: This study showed that FRAX was able to predict major osteoporotic fractures in individuals with reduced kidney function; further study is needed before FRAX should be routinely used in individuals with reduced kidney function.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".