P2742Does concordance last over years? From training exercise to practice in the SUCCOUR trial
Bibliographic record
Abstract
Background: Because global longitudinal strain (GLS) has shown greater sensitivity and lower variability than EF, the use of it is recommended in cardio-oncology guidelines. Previous work has documented that a quality control process improves inter-observer concordance of GLS, which exceeds that of EF. However, it is unknown whether the concordance persists subsequently. Purpose: To show the stability of training over time would be valuable in both research and practice Methods: To standardise GLS measurement, we administered a two-stage baseline calibration session with tailored feedback to 18 independent strain readers at 17 different sites in a multi-national randomised controlled trial. This study involved the consistency of GLS between the sites and core lab (CL) over 6 month follow-up (6MFU). Coefficient of variance (CV) was used to determine concordance. Results: 18 readers had completed the calibration and the CV of GLS at 1st and 2nd session were 4.5±3.1% and 3.9±2.9%. In the trial, 71 patients (54±13 years, 66 female) were enrolled. The time delay between training and initial analysis was 20±23 weeks. Baseline GLS of the overall group at CL and sites (-21.1±2.4% vs -20.3±1.9%, p=0.16) decreased at 6MFU (-19.8±2.5% vs -19.3±2.6%, p=0.85). The CV of GLS at baseline and 6MFU were 4.9±3.5% and 4.9±4.5% (p=0.96). In the whole period from 1st calibration to the 6MFU, the CV was stable and did not significantly change (p=0.19) (Figure). In contrast, the CV of EF at 1st session, baseline & 6MFU were 8.5±7.2, 4.7±3.3 & 6.1±5.0%, respectively and those tended to exceed that of GLS at each time-point (1st Cal: p<0.001, baseline: p=0.78, 6MFU: p=0.07, respectively).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".