Intervendor consistency and reproducibility of left ventricular 2D global and regional strain with two different high-end ultrasound systems
Bibliographic record
Abstract
AIMS: We aimed to assess intervendor agreement of global (GLS) and regional longitudinal strain by vendor-specific software after EACVI/ASE Industry Task Force Standardization Initiatives for Deformation Imaging. METHODS AND RESULTS: Fifty-five patients underwent prospective dataset acquisitions on the same day by the same operator using two commercially available cardiac ultrasound systems (GE Vivid E9 and Philips iE33). GLS and regional peak longitudinal strain were analyzed offline using corresponding vendor-specific software (EchoPAC BT13 and QLAB version 10.3). Absolute mean GLS measurements were similar between the two vendors (GE -17.5 ± 5.2% vs. Philips -18.9 ± 5.1%, P = 0.15). There was excellent intervendor correlation of GLS by the same observer [r = 0.94, P < 0.0001; bias -1.3%, 95% CI limits of agreement (LOA) -4.8 to 2.2%). Intervendor comparison for regional longitudinal strain by coronary artery territories distribution were: LAD: r = 0.85, P < 0.0001; bias 0.5%, LOA -5.3 to 6.4%; RCA: r = 0.88, P < 0.0001; bias -2.4%, LOA -8.6 to 3.7%; LCX: r = 0.76, P < 0.0001; bias -5.3%, LOA -10.6 to 2.0%. Intervendor comparison for regional longitudinal strain by LV levels were: basal: r = 0.86, P < 0.0001; bias -3.6%, LOA -9.9 to 2.0%; mid: r = 0.90, P < 0.0001; bias -2.6%, LOA -7.8 to 2.6%; apical: r = 0.74; P < 0.0001; bias -1.3%, LOA -9.4 to 6.8%. CONCLUSIONS: Intervendor agreement in GLS and regional strain measurements have significantly improved after the EACVI/ASE Task Force Strain Standardization Initiatives. However, significant wide LOA still exist, especially for regional strain measurements, which remains relevant when considering vendor-specific software for serial measurements.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".