Performance of Risk Indices for Identifying Low Bone Mineral Density and Osteoporosis in Mexican Mestizo Women with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: We evaluated the utility of 6 generic and 2 specific risk indices for identifying low bone mineral density (BMD) or osteoporosis in women with rheumatoid arthritis (RA); and their correlation with 10-year probability of fractures as assessed with the World Health Organization fracture risk assessment (FRAX) tool. METHODS: Mexican Mestizo women with RA were evaluated in this cross-sectional study using 6 generic indices [Simple Calculated Osteoporosis Risk Estimation (SCORE); Osteoporosis Risk Assessment Instrument (ORAI); Osteoporosis Self-Assessment Tool; Age, Body Size, No Estrogen; Osteoporosis Index of Risk (OSIRIS); and Guidelines of the US National Osteoporosis Foundation], 2 specific indices (Amsterdam and modified Amsterdam), and FRAX. BMD results on dual-energy x-ray absorptiometry (DEXA) at the lumbar spine and femoral neck were considered the "gold standard." Sensitivity, specificity, and predictive values (PV) of the indices and their correlations with FRAX results were estimated. RESULTS: Among 191 patients, 46 had osteoporosis (24.1%) and 119 had low BMD (62.3%). For predicting osteoporosis, SCORE showed the highest sensitivity (96%), whereas OSIRIS (87%) and ORAI (82%) showed the highest specificities. OSIRIS also had the greatest positive PV (92%). The specific indices had low sensitivity and low specificity (Amsterdam, 50% and 79%, respectively; modified Amsterdam, 56% and 70%). All the indices had a low but significant correlation with FRAX. CONCLUSION: These findings support the use of some generic indices to identify patients with RA who should undergo DEXA testing. Currently available specific indices did not perform satisfactorily. New specific risk indices for osteoporosis in RA should be developed to increase sensitivity and specificity for predicting osteoporosis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".