Impact of Sublingual Nitroglycerin on the Assessment of Computed Tomography–derived Fractional Flow Reserve: An Intraindividual Comparison Study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate the impact of nitroglycerin (NTG) on the assessment of computed tomography-derived fractional flow reserve (CT-FFR). MATERIALS AND METHODS: Seventy-seven patients with suspected coronary artery disease were recruited, and they underwent computed tomography angiography (CCTA) before and after NTG administration. The CT-FFRs were compared at 2 CCTAs. The difference was compared using the Wilcoxon signed rank test. Patients were divided into normal and stenosis groups according to CCTA results. Vessels in the stenosis group were further divided into different groups based on coronary artery calcium score (CACS) and stenosis degree. The poststenotic CT-FFR differences before and after NTG (DCT-FFR) were calculated to evaluate the impact of stenosis degree and CACS. Terminal CT-FFRs derived from CCTAs before and after NTG in total and vessel-specific levels were compared in the normal group. RESULTS: Of 47 patients in the stenosis group, poststenotic CT-FFR was significantly increased after NTG at per-vessel level. By taking CT-FFR of 0.75 or lower as the threshold, 5 and 4 patients showed abnormal CT-FFR before and after NTG, respectively. No significant differences were noted among the various stenosis degree and CACS groups regarding DCT-FFR. Of 30 patients in the normal group, terminal CT-FFR was significantly increased after NTG in total level and vessel-specific level of left anterior descending and right coronary artery, but not in the left circumflex. CONCLUSIONS: Both post lesion and distal vessel CT-FFR significantly improved after the administration of GTN with the degree of change not affected by stenosis severity or CACS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".