Reproducibility assessment of biventricular strain derived from Long-Axis feature tracking in travelling Volunteers - A study in the Berlin research network for cardiovascular magnetic resonance (BER-CMR)
Bibliographic record
Abstract
PURPOSE: To evaluate the reproducibility of biventricular global longitudinal strain (GLS) assessment using cardiovascular magnetic resonance in a multicenter study of travelling volunteers. METHODS: Twenty travelling volunteers were prospectively scanned at four sites with same-vendor scanners at 3.0T (sites I, II, III) and 1.5T (site IV). Cine imaging in three long-axis views was performed using a segmented balanced steady-state free precession sequence with 30 cardiac phases except site II with 25 phases. RESULTS: Imaging and post-processing were carried out successfully for 18 volunteers in a core lab setting. Pairwise comparisons revealed significant differences in left ventricular (LV) GLS between sites I and II (p < 0.001) and sites II and IV (p = 0.013), as well as in right ventricular (RV) GLS between sites I and IV (p = 0.027). RV GLS values were significantly higher at 3.0T (p = 0.024), whereas field strength had no significant impact on LV GLS (p = 0.153). Conversely, the use of 25 cardiac phases at site II was associated with significantly lower LV GLS values (p < 0.001), while RV GLS remained unaffected (p = 0.825). CONCLUSION: When applying feature tracking-based strain in a multicenter study, careful consideration should be given to the temporal resolution for LV longitudinal strain and to magnetic field strength for RV longitudinal strain.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".