Discordances between predicted and actual risk in obese patients with suspected cardiac ischaemia
Bibliographic record
Abstract
OBJECTIVES: To test the relationship between increasing severity of obesity, calculated risk and observed outcomes. METHODS: Patients with symptoms suggestive of coronary artery disease (CAD) (n=10 003) were stratified according to body mass index (BMI). We compared risk factors, pooled risk scores and physicians' perception of risk. Cox regression tested the association between BMI and (1) presence of obstructive CAD and (2) composite clinical endpoints (death, cardiovascular death, unstable angina hospitalisation and myocardial infarction). RESULTS: in 48% of patients and ≥35 in 20%. Increasingly obese patients were younger, female and non-smoking but with higher prevalence of hypertension, diabetes, black race and sedentary lifestyle. Pooled risk estimates of CAD were highest in those with mid-range BMI. In contrast, physicians' estimation of the likelihood of significant CAD based on clinical impression increased progressively with BMI. For a 10% increase in the Diamond-Forrester probability of CAD, the adjusted OR for obstructive CAD was 1.5 (95% CI 1.4 to 1.5) in patients with BMI <35, but only 1.2 (95% CI 1.1 to 1.3) in those with BMI ≥35 (interaction p<0.001). Framingham Risk Score increased across increasing BMI categories. However, there was a strong and consistent inverse relationship between degree of obesity and all three composite clinical endpoints over a median 25 months of follow-up. CONCLUSIONS: Despite perceptions of higher risk and higher risk scores, increasingly obese patients had obstructive CAD less frequently than predicted and had fewer adverse clinical outcomes. There is a need for risk assessment tools and guidelines that account for obesity. TRIAL REGISTRATION NUMBER: NCT01174550.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".