Validation of the FRAX® algorithm with adjustments for women living with diabetes from a Brazilian cohort study
Bibliographic record
Abstract
In recent decades, an increased risk of fractures in people living with diabetes has been described. This risk appears to be independent of bone mineral density. Therefore, screening instruments for fracture risk have been proposed in this population. The FRAX® algorithm is one of them. However, the FRAX® algorithm originally described does not consider non-insulin dependent diabetic status as a risk predictor. Some studies suggest that minor modifications to this instrument can improve its performance. These modifications appear to work well in other countries, such as Canada. However, we do not know whether these adjustments work in the Brazilian population. The objective of our study was to evaluate the calibration and accuracy of the FRAX® algorithm with and without adjustments for women living with diabetes. Methods: To this end, a cohort study was carried out that included women receiving primary care in the city of Santa Maria, RS. The risk for major and hip fractures was calculated using the FRAX® tool based on diagnoses confirmed by imaging tests and medical reports.The FRAX® risk was calculated: 1) Without adjustments (unadjusted FRAX®); 2) Increasing the entered age by ten years in individuals with diabetes (FRAX® 10 years); 3) Inserting the diagnosis of diabetes as rheumatoid arthritis (FRAX® AR). Of the 1,301 women eligible to participate in the study, 1,057 were enrolled, and 854 completed the 5-year follow-up. There were no differences between the area under the ROC curve of the unadjusted calculated FRAX® score and the FRAX® 10-year or FRAX® AR for major and hip fractures. The accuracy for major fracture was 0.948 (unadjusted FRAX®), 0.947 (FRAX® 10 years) and 0.946 (FRAX® AR). Furthermore, for hip fractures, the accuracies were 0.989 (unadjusted FRAX®), 0.988 (FRAX® 10 years) and 0.988 (FRAX® AR). On the other hand, both the FRAX® 10 years and the FRAX® AR were better calibrated, presenting a lower Chi- square. The calibration for major and hip fractures was, respectively, 15.4 (FRAX® unadjusted), 14.4 (FRAX® 10 years) and 14.4 (FRAX® AR), and 0.87 (unadjusted FRAX®), 0.32 (FRAX® 10 years) and 0.69 (FRAX® AR). In conclusion, the FRAX algorithm showed good accuracy in women living with diabetes followed in primary care. The FRAX® 10-year and FRAX® AR settings were better calibrated in this population. These data suggest that using the FRAX® tool with adjustments could help identify the risk of fractures in WLDM in primary care in Brazil.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".