Bibliographic record
Abstract
Kawasaki disease (KD) is an acute, self-limited vasculitis affecting young children. It can result in coronary artery abnormalities in a significant proportion of patients, especially if the diagnosis is missed or treatment gets delayed. Echocardiography is the imaging modality of choice for detection of coronary artery abnormalities and assessment of myocardial function. It is also useful for characterization and risk stratification of patients with KD. Echocardiography should be performed at the time of diagnosis and then again at 1-2 weeks and 4-6 weeks after treatment, for uncomplicated cases who do not have significant coronary artery involvement. Use of a standardized imaging protocol is necessary to detect and characterize coronary artery abnormalities, including standardization of measurements (Z scores). For patients with evolving abnormalities, more frequent assessment is necessary in order to detect thromboses in aneurysms. Long-term prognosis and management is dependent on both the maximal and current Z scores of aneurysms. Patients with large or giant aneurysms (i.e., Z score ≥ 10) are at the highest risk of both thrombosis and stenosis. Such patients need careful follow-up for subsequent cardiovascular events. Many of them would be candidates for advanced cardiovascular imaging and may require revascularization therapy. Serial echocardiography plays a key role in surveillance. In addition, stress echocardiography has proven useful as a modality to assess for inducible myocardial ischemia. Intravascular ultrasound has been recommended for functional and structural assessment of coronary arteries in children with KD.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".