P3326Diagnostic yield of cardiovascular magnetic resonance in the screening of relatives to patients with arrhythmogenic right ventricular cardiomyopathy
Bibliographic record
Abstract
Background: As a part of the revision of the Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) Task Force Criteria in 2010 (2010 TFC), more specific cardiovascular magnetic resonance (CMR) parameters were included due to the ability to provide accurate information about anatomy and functionality of the right ventricle. All ARVC probands identified at our institution are offered family screening including CMR as a part of clinical screening for ARVC. The aim of this study was to evaluate the clinical value of CMR as a part of the screening program in relatives to patients with AVRC. Methods: The retrospective cohort study registered data from relatives to ARVC probands. All included subjects have had a CMR as a part of ARVC screening in the period from January 1st 2010 to September 1st 2016. Patient data were registered using the 2010 TFC and included results from non-invasive examinations (ECG, signal-averaged ECG, Holter, echocardiogram, CMR and genetics) and baseline information such as gender, age, and occurrence of cardiovascular symptoms. CMR scans were performed on a 1.5-Tesla scanner (Magnetom Avanto, Siemens, Germany) and all Images were analyzed independently using CVI42 (Circle Cardiovascular Imaging, Canada).
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".