Cardiovascular magnetic resonance feature-tracking assessment of myocardial mechanics: Intervendor agreement and considerations regarding reproducibility
Bibliographic record
Abstract
AIM: To assess intervendor agreement of cardiovascular magnetic resonance feature tracking (CMR-FT) and to study the impact of repeated measures on reproducibility. MATERIALS AND METHODS: Ten healthy volunteers underwent cine imaging in short-axis orientation at rest and with dobutamine stimulation (10 and 20 μg/kg/min). All images were analysed three times using two types of software (TomTec, Unterschleissheim, Germany and Circle, cvi(42), Calgary, Canada) to assess global left ventricular circumferential (Ecc) and radial (Err) strains and torsion. Differences in intra- and interobserver variability within and between software types were assessed based on single and averaged measurements (two and three repetitions with subsequent averaging of results, respectively) as determined by Bland-Altman analysis, intraclass correlation coefficients (ICC), and coefficient of variation (CoV). RESULTS: Myocardial strains and torsion significantly increased on dobutamine stimulation with both types of software (p<0.05). Resting Ecc and torsion as well as Ecc values during dobutamine stimulation were lower measured with Circle (p<0.05). Intra- and interobserver variability between software types was lowest for Ecc (ICC 0.81 [0.63-0.91], 0.87 [0.72-0.94] and CoV 12.47% and 14.3%, respectively) irrespective of the number of analysis repetitions. Err and torsion showed higher variability that markedly improved for torsion with repeated analyses and to a lesser extent for Err. On an intravendor level TomTec showed better reproducibility for Ecc and torsion and Circle for Err. CONCLUSIONS: CMR-FT strain and torsion measurements are subject to considerable intervendor variability, which can be reduced using three analysis repetitions. For both vendors, Ecc qualifies as the most robust parameter with the best agreement, albeit lower Ecc values obtained using Circle, and warrants further investigation of incremental clinical merit.
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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 | 0.057 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".