ICEBERG-3: carotid plaque score combined with the stress echo improves 5-year major adverse cardiovascular events risk prediction
Bibliographic record
Abstract
Aims: Stress echocardiography (SE), though widely accessible, has some limitations in its diagnostic test characteristics for predicting major adverse cardiovascular events (MACEs). Carotid plaque score provides direct detection of subclinical atherosclerosis and can be integrated into the stress protocol. The aim of our study was to assess the value of adding a carotid plaque score to SE to enhance the test diagnostics for predicting MACE in low-intermediate-risk patients. Methods and results: Patients aged 40-75 years referred for SE received a carotid ultrasound and were followed for 5-year MACE. Hard MACE was defined as a composite of cardiovascular death, non-fatal stroke or myocardial infarction, and emergency coronary revascularization. Soft MACE included non-emergency coronary revascularization. Patients aged >75 years, on a statin, with previously known vascular disease, a history of stroke, myocardial infarction, vascular intervention, or a resting wall motion abnormality on a baseline echo were excluded. Administrative data holdings housed at the Institute of Clinical Evaluative Sciences, ICES, were used for event follow-up. Of the 2588 patients, there were 49 cumulative incidence hard MACE and 119 soft MACE. Carotid plaque score improved the sensitivity of SE for predicting 1- and 5-year MACE. A plaque score threshold value of ≥2 provided clear differentiation of patients who experienced MACE in both positive and non-positive (negative/inconclusive for ischaemia) SE results. Conclusion: Plaque score enhances diagnostic test characteristics of SE. The combination of carotid ultrasound with SE is an important new tool for cardiovascular risk assessment. This simple tool may help differentiate risk in patients with non-positive SE results.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".