The strain-8 study: a multimodal scan–rescan assessment of myocardial strain repeatability
Bibliographic record
Abstract
Aims: Myocardial strain is a powerful, non-invasive diagnostic and prognostic marker in patients with heart disease. However, its applicability is hindered by uncertain repeatability, particularly for segmental values. This study measures the repeatability of myocardial strain across eight imaging methods. Methods and results: 8, 14 men) were recruited and scanned twice with eight strain imaging protocols: cardiac magnetic resonance (CMR) at 1.5T and 3T with cine, tagging, and displacement encoding with stimulated echoes (DENSE) sequences, and 2D and 3D echocardiography (Echo). Global and segmental strains were quantified from each scan. Inter-scan repeatability was assessed with the coefficient of variation (CoV), intraclass correlation coefficient, and Bland-Altman analysis. Results: Inter-scan repeatability of global strains ranged from excellent to fair (CoV ≤ 20%) depending on protocol. Using CMR feature tracking at 1.5T, relative global longitudinal strain (GLS) changes exceeding 11.2% are unlikely to be caused by measurement variability alone; this figure is 5.5% for 2D echocardiography. Segmental strain values frequently had poor repeatability (CoV > 20%), particularly for longitudinal and radial strains. Conclusion: Imaging protocols including CMR and Echo can measure global strain parameters with fair repeatability, but segmental strain values are unreliable. Future work should aim to improve the repeatability of segmental strain values, particularly 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".