Prognostic value of coronary CT angiography: lessons from the West Denmark Cardiac Registry
Bibliographic record
Abstract
This editorial refers to ‘Prognostic assessment of stable coronary artery disease as determined by coronary computed tomography angiography: a Danish multicentre cohort study’†, by L.H. Neilsen et al., on page 413. Somewhere, something incredible is waiting to be known Carl Sagan The last decade has seen a veritable explosion of data evaluating and establishing the prognostic value of coronary computed tomography angiography (CTA) in asymptomatic and symptomatic patients with suspected coronary artery disease (CAD).1–3 These data have been essential to move coronary CTA from simply a diagnostic test to one that is rich in prognostic information. The first manuscripts were, however, quite limited in their scope, emanating from single centres and therefore fraught with all of the inclusion and ascertainment biases that are inherent in retrospective single-centre analyses.4,5 Building upon these initial experiences, large registries were developed, led by the CONFIRM registry and James Min which have served to deepen our understanding of the relationship of baseline cardiovascular risk, symptoms, and coronary CT findings and downstream major adverse cardiovascular events (Figure 1).2 These registries have confirmed that a worsening extent and severity of CAD on CTA results in increased relative and absolute risk to the patient across gender and a whole host of other subanalyses.
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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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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".