Bibliographic record
Abstract
Modern advanced imaging techniques have allowed increasingly more rigorous assessment of the cardiac structure and function of several types of cardiomyopathies. In contemporary cardiology practice, echocardiography and cardiac magnetic resonance imaging are widely used to provide a basic framework in the evaluation and management of cardiomyopathies. Echocardiography is the quintessential imaging technique owing to its unique ability to provide real-time images of the beating heart with good temporal resolution, combined with its noninvasive nature, cost-effectiveness, availability, and portability. Cardiac magnetic resonance imaging provides data that are both complementary and uniquely distinct, thus allowing for insights into the disease process that until recently were not possible. The new catchphrase in the evaluation of cardiomyopathies is multimodality imaging, which is purported to be the efficient integration of various methods of cardiovascular imaging to improve the ability to diagnose, guide therapy, or predict outcomes. It usually involves an integrated approach to the use of echocardiography and cardiac magnetic resonance imaging for the assessment of cardiomyopathies, and, on occasion, single-photon emission computed tomography and such specialized techniques as pyrophosphate scanning.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".