Inter- and Intraoperator Variability in Measurement of On-Site CT-derived Fractional Flow Reserve Based on Structural and Fluid Analysis: A Comprehensive Analysis
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Bibliographic record
Abstract
Purpose To measure the inter- and intraobserver variability among operators of varying expertise in conducting CT-derived fractional flow reserve (CT FFR) measurements on-site by using structural and fluid analysis and to evaluate differences in reproducibility between two different training methods for end users. Materials and Methods This retrospective analysis of the prospectively enrolled cohort included 22 symptomatic patients who underwent both 320–detector row coronary CT angiography and catheter-derived fractional flow reserve (FFR) within 90 days. Thirteen operators of varying expertise were assigned to one of two training arms: arm 1, on-site training by a specialist in CT FFR technology; arm 2, self-training through use of written materials. After the training, all 13 operators reviewed the CT data and measured CT FFR in 24 vessels in 22 patients. Inter- and intraoperator variability and agreements between CT FFR and catheter-derived FFR measurements were evaluated. Results The overall intraclass correlation coefficient (ICC) among operators was 0.71 (95% confidence interval: 0.58, 0.83) with a mean absolute difference (± standard deviation) of 0.027 ± 0.022. The operators in arm 2 showed greater interoperator differences than those in arm 1 (0.031 ± 0.024 vs 0.023 ± 0.018; P = .024). Among operators who recalculated CT FFR, the mean CT FFR value did not significantly differ between the first and second calculations (ICC, 0.66; 95% confidence interval: 0.46, 0.87), with the medical specialists producing the lowest intraoperator variability (0.053 ± 0.060). The overall correlation coefficient between CT FFR and catheter FFR was r = 0.61, with a mean absolute difference of 0.096 ± 0.089. Conclusion Good reproducibility of CT FFR values calculated on-site on the basis of structural and fluid analysis was observed among operators of varying expertise. Face-to-face training sessions may cause less variability. Keywords: Adults, CT-Angiography, Cardiac, Computer Applications-General (Informatics), Coronary Arteries © RSNA, 2019 Supplemental material is available for this article.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it