Abstract 15182: Calibration and Feedback of Speckle Strain Measurement Improves Segmental Strain Concordance
Bibliographic record
Abstract
Purpose: Inter-institutional agreement of imaging measurements is important in shared clinical management and in trials. 2D strain is sensitive to changes in LV function, automated and may be less variable than ejection fraction (EF), which is used widely but has inherent variability. We sought whether strain would have better concordance between different centers and that feedback from a calibration exercise would reduce the variability among institutions. Methods: 108 global longitudinal strain (GLS) measurements, calculated from 1944 segmental strains, were performed blindly by 21 experienced readers from 12 different institutes (5 Europe, 5 Asia, 1 North America and 1 Australia) in 6 cases. Intraclass correlation coefficients (ICCs) were used to determine concordance. All individual measurements were reviewed and some key points were identified to optimize strain measurement. After feedback, strain was remeasured and improvement of agreement sought using coefficient variance (CV) and mean difference (MD) from the reference standard. Results: GLS was -17.9±3.3%, while EF was 60±7%. The ICC in GLS (0.994 [95%CI 0.983, 0.999]) was better than that of 2DEF (0.928 [0.809, 0.988], p<0.001) at baseline. Two main sources of discordance in GLS measurements were the width and location of regions of interest, especially at mitral annulus and apex. After feedback, re-measurement showed the CV (p=0.02) and MD (p=0.03) of segmental strain improved, but comparison of 2nd vs 1st GLS showed no changes of ICC (p=0.85), CV (p=0.80) or MD (p=0.92). Conclusions: Feedback significantly decreased segmental strain variability, but did not improve the concordance in GLS, which had better precision than EF at baseline.
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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 | 0.016 | 0.055 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".