Stress Echocardiography: Concept and Criteria, Structure and Steps, Obstacles and Outcomes, Focused Update and Review
Bibliographic record
Abstract
Stress echocardiography (SEC) is a technique established more than 35 years ago; however, it is still poorly implemented in many countries and institutions, and this reluctance may be related to many obstacles such as operator skills, lack of awareness or institutional policy. Stress echo was initially used for assessing coronary artery disease (CAD), with respect to myocardial viability, using wall motion response; however, current use of stress echo extends beyond CAD, such as valvular heart disease and diastolic stress test. Dobutamine is a commonly used agent when pharmaceutical approach is implemented. With regard to CAD, there are four stress responses: normal, ischemic, viable and necrotic. A low dose dobutamine protocol is recommended in patients with baseline wall motion abnormalities, and a very low dose dobutamine is used in low flow low gradient aortic stenosis in order to check the flow and contractility reserve. Of note, respecting protocols, indications and contraindications are important to avoid or minimize risks of complications during the procedure. This article presents a focused update and review regarding SEC, along with an overview of the different indications, structures and steps, and obstacles and outcomes; also the article aims to highlight more awareness and sensitization on this useful technique.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".