ESC pre-test probability estimates for obstructive coronary artery disease: can they be used in Brazil?
Bibliographic record
Abstract
Aims: Cardiovascular disease, primarily coronary artery disease (CAD), is the leading cause of mortality worldwide. Accurate diagnosis of CAD often requires pre-test probability (PTP) estimation, traditionally performed using scoring systems like the Diamond-Forrester (DF) and European Society of Cardiology (ESC) models. However, the applicability of such models in specific populations may vary. This study compares the performance of DF and PTP scores in the Brazilian context, using coronary computed tomography angiography (CCTA) as a reference standard. Methods and results: PTP for obstructive CAD was calculated using DF and ESC scores in 409 symptomatic patients without known CAD who underwent CCTA between 2019 and 2022. Predicted PTP was compared with actual CAD prevalence. DF overestimated CAD prevalence across age and symptom categories, while ESC showed better alignment with actual prevalence. Conclusion: Our study confirms that the ESC PTP model is more appropriate than the DF model for determining PTP in the Brazilian population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".