Automated abdominal aortic calcification and major adverse cardiovascular events in people undergoing osteoporosis screening: the Manitoba Bone Mineral Density Registry
Bibliographic record
Abstract
Vertebral fracture assessment (VFA) images from bone density machines enable the automated machine learning assessment of abdominal aortic calcification (ML-AAC), a marker of cardiovascular disease (CVD) risk. The objective of this study was to describe the risk of a major adverse cardiovascular event (MACE, from linked health records) in patients attending routine bone mineral density (BMD) testing and meeting specific criteria based on age, BMD, height loss, or glucocorticoid use have a VFA in the Manitoba BMD Registry. The cohort included 10 250 individuals (mean age 75.5 yr, 94% women without CVD) with VFA (February 2010 to March 2017). ML-AAC24 scores were categorized (low <2; moderate 2-<6; high ≥6). Over follow-up (mean 3.9 yr), 1265 people (12.3%) experienced a MACE. Among those with low, moderate, and high ML-AAC24, MACE rates per 1000 person-years were 18.4 (95% CI 16.4-20.5), 34.1 (95% CI 30.9-37.4), and 55.6 (95% CI 50.8-60.1), respectively. A similar gradient was observed after stratifying by age and sex. Incidence rate ratios (IRRs) for low vs moderate and high groups were 1.9 (95% CI 1.6-2.2) and 3.0 (95% CI 2.6-3.5), respectively. In those most likely to benefit from pharmaceutical intervention (<80 yr, not on statins), MACE rates among those with low, moderate, and high ML-AAC24 were 13.5 (95% CI 11.5-15.8), 26.0 (95% CI 22.1-30.3) and 44.1 (95% CI 37.0-52.0). Corresponding IRRs for low vs moderate 1.9 (95% CI 1.5-2.4) and high ML-AAC24 was 3.3 (95% CI 2.6-4.1]), respectively. In routine osteoporosis screening, individuals with moderate and high ML-AAC24 had substantially greater MACE rates compared to those with low ML-AAC24. Consequently, AAC detection during osteoporosis screening (especially in women) may guide intensification of preventative cardiovascular strategies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".