Combining Frailty and Trabecular Bone Score Did Not Improve Predictive Accuracy in Risk of Major Osteoporotic Fractures
Bibliographic record
Abstract
ABSTRACT It is recognized that the trabecular bone score (TBS) provides skeletal information, and frailty measurement is significantly associated with increased risks of adverse health outcomes. Given the suboptimal predictive power in fracture risk assessment tools, we aimed to evaluate the combination of frailty and TBS regarding predictive accuracy for risk of major osteoporotic fracture (MOF). Data from the prospective longitudinal study of CaMos (Canadian Multicentre Osteoporosis Study) were used for this study. TBS values were estimated using lumbar spine (L1 to L4) dual-energy X-ray absorptiometry (DXA) images; frailty was evaluated by a frailty index (FI) of deficit accumulation. Outcome was time to first incident MOF during the follow-up. We used the Harrell's C-index to compare the model predictive accuracy. The Akaike information criterion, likelihood ratio test, and net reclassification improvement (NRI) were used to compare model performances between the model combining frailty and TBS (subsequently called “FI + TBS”), FI-alone, and TBS-alone models. We included 2730 participants (mean age 69 years; 70% women) for analyses (mean follow-up 7.5 years). There were 243 (8.90%) MOFs observed during follow-up. Participants with MOF had significantly higher FI (0.24 versus 0.20) and lower TBS (1.231 versus 1.285) than those without MOF. FI and TBS were significantly related with MOF risk in the model adjusted for FRAX with bone mineral density (BMD) and other covariates: hazard ratio (HR) = 1.26 (95% confidence interval [CI] 1.11–1.43) for per-SD increase in FI; HR = 1.38 (95% CI 1.21–1.59) for per-SD decrease in TBS; and these associations showed negligible attenuation (HR = 1.24 for per-SD increase in FI, and 1.35 for per-SD decrease in TBS) when combined in the same model. Although the model FI + TBS was a better fit to the data than FI-alone and TBS-alone, only minimal and nonsignificant enhancement of discrimination and NRI were observed in FI + TBS. To conclude, frailty and TBS are significantly and independently related to MOF risk. Larger studies are warranted to determine whether combining frailty and TBS can yield improved predictive accuracy for MOF risk. © 2020 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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".