Prevalent vertebral fracture is associated with incident cardiovascular disease events in older individuals referred for bone densitometry
Bibliographic record
Abstract
BACKGROUND: It is unknown if prevalent vertebral fracture (PVFx) captured on bone density vertebral fracture assessment (VFA) images predicts incident CVD events. METHODS: 11,760 individuals (mean [SD] age 75.7 [6.8] years, 94 % female) had VFA contemporaneously with bone densitometry in Manitoba, Canada, between February 2010 and December 2017, of whom 1919 (16.3 %) had ≥1 PVFx. This cohort was followed over a mean (SD) 3.8 (2.3) years for Major Adverse Cardiovascular Events (MACE, composite of hospitalization for myocardial infarction, non-hemorrhagic stroke, or all-cause mortality) and other CVD events (hospitalization for coronary artery disease, congestive heart failure, peripheral vascular disease, or coronary bypass/stenting). Proportional hazards models were used to estimate hazard ratios (HR) for incident MACE and other CVD events in those with compared to those without PVFx. RESULTS: Adjusted for age and sex, those with PVFx had HR for incident MACE of 1.34 (95 % C·I. 1.19, 1.51), hospitalization for myocardial infarction (HR 1.35, 95 % C.I. 1.02, 1.79), all-cause mortality (HR 1.36, 95 % C.I, 1.19, 1.56), and other CVD events (HR 1.40, 95 % C.I. 1.21, 1.61). These associations were only slightly attenuated with further adjustment for prior CVD disease and additional CVD risk factors. CONCLUSION: Prevalent vertebral fracture identified on VFA images in routine clinical practice is robustly associated with incident MACE, independent of other risk factors including AAC which can be simultaneously ascertained on the same images. VFA may have utility for prediction of fractures and CVD outcomes via ascertainment of both prevalent vertebral fracture and AAC.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".