Intracoronary ultrasound imaging: methods and clinical applications.
Bibliographic record
Abstract
OBJECTIVE: To review the development of intracoronary ultrasound, its current utility and the impetus for its continued development as a coronary imaging modality. DATA SOURCES: English-language literature (1966 to 1999) was searched in the MEDLINE database with the key words 'ultra- sound', 'intravascular' and 'intracoronary', and limited to human studies. In addition, an online public access catalogue was searched using the subject headings 'cardiovascular diseases - therapy', 'heart diseases' and 'vascular diseases'. STUDY SELECTION AND DATA EXTRACTION: Articles relating to the history of intravascular or intracoronary ultrasound, methods and materials employed, advantages and disadvantages, safety issues and future directions of research in the area of intracoronary ultrasound were selected. DATA SYNTHESIS: Intracoronary ultrasound has been shown to improve upon demonstrated weaknesses of coronary angiography. This imaging technique, while invasive, has not been associated with significant, acute adverse effects and has proved to be useful in guiding interventions, and evaluating the mechanism and extent of their success. Technological limitations with respect to the equipment employed, and the acquisition, processing and display of images are the subject of intense research focus because they hinder more widespread clinical use of intracoronary ultrasound. CONCLUSIONS: Intracoronary ultrasound has emerged as a safe and useful tool in the visualization of the coronary vasculature. Technological limitations and questions about long term safety are a concern. Its ability to overcome the inherent limitations of coronary angiography, and to guide and evaluate coronary interventions supports the notion that this technique will continue to assume an ever-expanding role in interventional cardiology.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".