Comparison of Methods for Improving Fracture Risk Assessment in Diabetes: The Manitoba BMD Registry
Bibliographic record
Abstract
Type 2 diabetes is a risk factor for fracture independent of FRAX (fracture risk assessment) probability. We directly compared four proposed methods to improve the performance of FRAX for type 2 diabetes by: (1) including the rheumatoid arthritis (RA) input to FRAX; (2) making a trabecular bone score (TBS) adjustment to FRAX; (3) reducing the femoral neck T-score input to FRAX by 0.5 SD; and (4) increasing the age input to FRAX by 10 years. We examined major osteoporotic fractures (MOFs) and hip fractures (HFs) over a mean of 8.3 years observation among 44,543 women and men 40 years of age or older (4136 with diabetes) with baseline lumbar spine and hip DXA from 1999 through 2016. Controlled for unadjusted FRAX probability, diabetes was associated with an increased risk for MOFs and HFs. All four FRAX adjustments attenuated the effect of diabetes, but a residual effect of diabetes was seen on MOF risk after TBS adjustment, and on HF risk after the RA and TBS adjustments. Among those with diabetes, unadjusted FRAX risk underestimated MOF (observed/predicted ratio 1.15; 95% CI, 1.03 to 1.28), but this was no longer significant after applying the diabetes adjustments. HF risk was more severely underestimated (observed/predicted ratio 1.85; 95% CI, 1.51 to 2.20) and was only partially corrected with the diabetes adjustments (still significant for the RA and TBS adjustments). Among those with diabetes, there was moderate reclassification based upon a fixed MOF cut-off of 20% (4.1% to 7.1%) or fixed HF cut-off of 3% (5.7% to 16.5%). Net reclassification improvement increased for MOF with each of the diabetes adjustments (range 3.9% to 5.6% in the diabetes subgroup). In conclusion, each of the proposed methods for addressing limitations in the ability of FRAX to assess fracture risk in individuals with diabetes was found to improve performance, though no single method was optimal in all settings. © 2018 American Society for Bone and Mineral Research.
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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 | 0.061 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".