Systemic and inflammatory disorders involving the heart: the role of PET imaging.
Bibliographic record
Abstract
Cardiac inflammatory disorders, either primarily cardiac or secondary to a systemic process, are associated with significant morbidity and/or mortality. Their diagnosis can be challenging, especially due to significant overlap in their clinical presentation with other cardiac diseases. Recent publications have investigated the potential diagnostic role of positron emission tomography (PET) imaging in these patients. Most of the available literature is focused on Fluorine-18 fluorodeoxyglucose (FDG), a tracer which has already demonstrated its use in other inflammatory and infectious processes. PET imaging can help in the diagnosis, prognosis and follow-up in a variety of cardiac inflammatory processes, including infective endocarditis, cardiac implantable electronic device infection, pericarditis, myocarditis, sarcoidosis and amyloidosis. PET's ability to depict metabolic changes and abnormalities, sometime even before the onset of any anatomical changes, can be a significant advantage over standard anatomical imaging. PET appears to be particularly useful in cases where standard investigation is non-diagnostic or equivocal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".