Clinical Management of Cardiac Sarcoidosis
Bibliographic record
Abstract
Approximately 5% of patients with sarcoidosis will have cardiac involvement clinically manifest as one or more of ventricular arrhythmias, conduction abnormalities and heart failure. Another 20% to 25% have clinically silent disease (asymptomatic cardiac involvement). There is a growing realisation that CS can be the first manifestation of sarcoidosis in any organ. In particular physicians should consider CS in patients with VT of unknown etiology and in patients aged <60 presenting with idiopathic advanced conduction system disease. Immunosuppression therapy (usually with corticosteroids) has been suggested for the treatment of clinically manifest CS despite modest data. Positron Emission Tomography (FDG-PET) imaging is often used to detect active disease and guide immunosuppression. The extent of left ventricular dysfunction seems to be the most important predictor of prognosis. Also the extent of LGE on CMR is emerging as an important prognostic factor. Patients with clinically manifest disease often need device therapy, usually with implantable cardioverter defibrillators. There are still much to be learned as regarding best practices in managing CS patients and multi-center research efforts are underway.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".