Derivation and validation of a novel functional FFRCT score incorporating the burden of coronary stenosis severity and flow impairment to predict clinical events
Bibliographic record
Abstract
Background A score combining the burden of stenosis severity on coronary computed tomography angiography (CCTA) and flow impairment by fractional flow reserve derived from computed tomography (FFR CT ) may be a better predictor of clinical events than either parameter alone. Methods The Functional FFR CT Score (FFS) combines CCTA and FFR CT parameters in an allocated point-based system. The feasibility of the FFS was assessed in cohort of 72 stable chest pain patients with matched CCTA and FFR CT datasets. Validation was performed using 2 cohorts: (a) 4468 patients from the ADVANCE Registry to define its association with revascularization and major adverse cardiovascular events (MACE); (b) 212 patients from the FORECAST trial to determine predictors of MACE. Results The median calculation time for the FFS was 10 (interquartile range 6–17) seconds, with strong intra-operator and inter-operator agreement (Cohen's Kappa 0.89 (±0.37, p < 0.001) and 0.83 (±0.04, p < 0.001, respectively). The FFS correlated strongly with both the CT-SYNTAX and the Functional CT-SYNTAX scores (rS = 0.808 for both, p < 0.001). In the ADVANCE cohort the FFS had good discriminatory abilities for revascularization with an area under the curve of 0.82, 95 % confidence interval (CI) 0.81–0.84, p < 0.001. Patients in the highest FFS tertile had significantly higher rates of revascularization (61 % vs 5 %, p < 0.001) and MACE (1.9 % vs 0.5 %, p = 0.001) compared with the lowest FFS tertile. In the FORECAST cohort the FFS was an independent predictor of MACE at 9-month follow-up (hazard ratio 1.04, 95 % CI 1.01–1.08, p < 0.01). Conclusion The FFS is a quick-to-calculate and reproducible score, associated with revascularization and MACE in two distinct populations of stable symptomatic patients.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".