Early diagnosis of acute coronary syndrome
Bibliographic record
Abstract
The diagnostic evaluation of acute chest pain has been augmented in recent years by advances in the sensitivity and precision of cardiac troponin assays, new biomarkers, improvements in imaging modalities, and release of new clinical decision algorithms. This progress has enabled physicians to diagnose or rule-out acute myocardial infarction earlier after the initial patient presentation, usually in emergency department settings, which may facilitate prompt initiation of evidence-based treatments, investigation of alternative diagnoses for chest pain, or discharge, and permit better utilization of healthcare resources. A non-trivial proportion of patients fall in an indeterminate category according to rule-out algorithms, and minimal evidence-based guidance exists for the optimal evaluation, monitoring, and treatment of these patients. The Cardiovascular Round Table of the ESC proposes approaches for the optimal application of early strategies in clinical practice to improve patient care following the review of recent advances in the early diagnosis of acute coronary syndrome. The following specific 'indeterminate' patient categories were considered: (i) patients with symptoms and high-sensitivity cardiac troponin <99th percentile; (ii) patients with symptoms and high-sensitivity troponin <99th percentile but above the limit of detection; (iii) patients with symptoms and high-sensitivity troponin >99th percentile but without dynamic change; and (iv) patients with symptoms and high-sensitivity troponin >99th percentile and dynamic change but without coronary plaque rupture/erosion/dissection. Definitive evidence is currently lacking to manage these patients whose early diagnosis is 'indeterminate' and these areas of uncertainty should be assigned a high priority for research.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".