Imaging the right heart: the use of integrated multimodality imaging
Bibliographic record
Abstract
During recent years, right ventricular (RV) structure and function have been found to be an important determinant of outcome in different cardiovascular and also pulmonary diseases. Currently, echocardiography and cardiac magnetic resonance (CMR) imaging are the two imaging modalities most commonly used to visualize the RV. Most structural abnormalities of the RV can be reliably described by echocardiography but due its complex geometrical shape, echocardiographic assessment of RV function is more challenging. Newer promising echocardiographic techniques are emerging but lack of validation and limited normal reference data influence their routine clinical application. Cardiac magnetic resonance is generally considered the clinical reference technique due to its unlimited imaging planes, superior image resolution, and three-dimensional volumetric rendering. The accuracy and reliability of CMR measurements make it the ideal tool for serial examinations of RV function. Multidetector computed tomography (MDCT) plays an important role in the diagnosis of pulmonary emboli but can also be used for assessing RV ischaemic disease or as an alternative for CMR if contra-indicated. Radionuclide techniques have become more obsolete in the current era. The different imaging modalities should be considered complimentary and each plays a role for different indications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".