CTA in the evaluation of acute chest pain syndromes. Should more widespread use be advocated?
Bibliographic record
Abstract
INTRODUCTION: With the advent of CT more than 3 decades ago, we have seen rapid evolution of this technology, so that we are now able to noninvasively accurately image the coronary arterial tree. This has opened up a debate as to the role of this imaging modality in our day-to-day evaluation of acute coronary syndromes. Much recent literature has focused on whether in the acute setting this modality should be incorporated into current evaluation and treatment guidelines. AREAS COVERED: A comprehensive review of a literature illustrating the utility of CTA in the acute care setting is presented. The paper goes on to address the benefits and challenges of implementation of CTA in the evaluation of acute chest pain syndromes. Alternative guidelines and insights on future directions are presented. EXPERT OPINION: In this current era where CAD, and more specifically acute chest pain syndromes, remains as a large part of ED visits and also healthcare costs, CTA will play an important role in the diagnosis and treatment of individuals. It remains only a matter of time when this will be implemented in our guidelines, in light of the recent literature and ever improving CTA protocols.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".