Rationale and design of the precise percutaneous coronary intervention plan (<scp>P3</scp>) study: Prospective evaluation of a virtual computed tomography‐based percutaneous intervention planner
Bibliographic record
Abstract
Abstract Introduction Fractional flow reserve (FFR) measured after percutaneous coronary intervention (PCI) has been identified as a surrogate marker for vessel related adverse events. FFR can be derived from standard coronary computed tomography angiography (CTA). Moreover, the FFR derived from coronary CTA (FFRCT) Planner is a tool that simulates PCI providing modeled FFRCT values after stenosis opening. Aim To validate the accuracy of the FFRCT Planner in predicting FFR after PCI with invasive FFR as a reference standard. Methods Prospective, international and multicenter study of patients with chronic coronary syndromes undergoing PCI. Patients will undergo coronary CTA with FFRCT prior to PCI. Combined morphological and functional evaluations with motorized FFR hyperemic pullbacks, and optical coherence tomography (OCT) will be performed before and after PCI. The FFRCT Planner will be applied by an independent core laboratory blinded to invasive data, replicating the invasive procedure. The primary objective is to assess the agreement between the predicted FFRCT post‐PCI derived from the Planner and invasive FFR. A total of 127 patients will be included in the study. Results Patient enrollment started in February 2019. Until December 2020, 100 patients have been included. Mean age was 64.1 ± 9.03, 76% were males and 24% diabetics. The target vessels for PCI were LAD 83%, LCX 6%, and RCA 11%. The final results are expected in 2021. Conclusion This study will determine the accuracy and precision of the FFRCT Planner to predict post‐PCI FFR in patients with chronic coronary syndromes undergoing percutaneous revascularization.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".