Nitroglycerin-Derived Pd/Pa for the Assessment of Intermediate Coronary Lesions.
Bibliographic record
Abstract
OBJECTIVES: To assess the predictive value of Pd/Pa after nitroglycerin administration (Pd/Pa[N]) as compared with standard fractional flow reserve (FFR). METHODS: Consecutive patients with intermediate coronary lesions assessed by FFR between January 2014 and October 2015 were included. We measured Pd/Pa at baseline, Pd/Pa(N), and Pd/Pa after incremental doses of intracoronary adenosine. RESULTS: A total of 134 patients (27% females; mean age, 65 years) were included. The diagnostic performance of Pd/Pa(N) and identification of cut-off value for Pd/Pa(N) compared with FFR threshold of 0.8 using receiver-operating characteristic (ROC) area under the curve analysis was between 0.98 (95% confidence interval, 0.95-1.00; P<.05) for 48 μg and 0.86 (95% confidence interval, 0.79-0.94; P<.05) for 240 μg adenosine. Pd/Pa(N) ≤0.8 had 100% positive predictive value. Pd/Pa(N) ≥0.94 provided 100% negative predictive value with a high sensitivity (>92%). Optimal diagnostic accuracy of Pd/Pa(N) was achieved for values ≤0.84. The Pearson's correlation between Pd/Pa(N) and FFR varied between 0.89 for 24 μg adenosine and 0.77 for 240 μg (P<.01). CONCLUSION: Pd/Pa(N) values can be used for diagnosis of hemodynamically significant lesions. Pd/Pa(N) correlates well with standard FFR. Pd/Pa(N) cut-off of ≤0.8 can be considered significant without need for adenosine injection. The value of using adenosine whenever Pd/Pa(N) is ≥0.94 is limited.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".