Bibliographic record
Abstract
PURPOSE OF REVIEW: The clinical role of emerging imaging technologies for diagnosing cardiac sarcoidosis and other cardiomyopathies is evolving. An up-to-date review of the role of various imaging modalities in the evaluation of cardiac sarcoidosis and other cardiomyopathies is presented. RECENT FINDINGS: No study prospectively established the accuracy of each of the various techniques for diagnosing myocardial involvement in patients with suspected cardiac sarcoidosis. Cardiac magnetic resonance imaging (CMR) is demonstrated to have a sensitivity of 100% and specificity of approximately 80%, and positive predictive value of approximately 55% in diagnosing cardiac sarcoidosis. Recent studies have shown that 18F-2-fluoro-2-deoxyglucose positron emission tomography (FDG-PET) has 100% sensitivity of detecting earlier stages of sarcoidosis. Both the FDG-PET and CMR may provide complementary information for the diagnosis and assessment of efficacy of therapy in patients with cardiac involvement from sarcoidosis. SUMMARY: Clinical and subclinical cardiac involvement is common among patients with sarcoidosis. A structured clinical assessment incorporating advanced cardiac imaging with CMR and FDG-PET scanning is more sensitive than the established clinical criteria. CMR is an established imaging modality in the diagnosis of various other cardiomyopathies. Well designed prospective clinical trials are awaited to define the exact role of these imaging studies in the diagnosis and guidance of therapy.
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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) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".