Multimodality imaging in pericardial diseases
Bibliographic record
Abstract
With a rapidly growing spectrum, non-specific symptoms and overlapping etiologies, pericardial diseases can represent a real diagnostic challenge. Consequently, multimodality imaging has taken a front seat in the diagnosis and management of these conditions. Cardiac CT offers an excellent anatomical characterization of pericardial thickening, fat stranding and/or presence of calcifications. and is also the preferred modality to assess extra-cardiac structures. Active pericardial inflammation, edema and fibrosis comprise pericardial characterization using CMR and allows for a precise diagnosis, disease staging and patient specific tailoring of therapies. PET scan still occupies a very modest role in the evaluation of pericardial diseases but might help discriminating malignant pericardial effusion and extrapulmonary tuberculous. More than ever, clinicians need to master how these modalities complement each other while avoiding unnecessary cost and to translate this knowledge into a more customized patient's care approach. The aim of this review was to recognize the role of multimodality imaging in the investigation of various pericardial diseases, assess how these modalities can impact the clinical course and treatment of these affections and finally elucidate their role in the patient's prognostication.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".