051 FRACTIONAL FLOW RESERVE AND THE INSTANT WAVE-FREE RATIO HAVE EQUIVALENT AGREEMENT WITH FLOW BASED INDICES ACROSS THE ENTIRE SPECTRUM OF STENOSIS SEVERITY RESULTS OF THE CLARIFY STUDY RESULTS OF CLARIFY
Bibliographic record
Abstract
Background The instantaneous wave-free ratio (iFR) is a vasodilator-free pressure-only measure of the haemodynamic severity of a coronary stenosis comparable to fractional flow reserve (FFR) in diagnostic categorisation. In this study we use hyperaemic stenosis resistance (HSR), a combined pressure-and-flow index as an arbiter to determine when iFR and FFR disagree, which index is most representative of the hemodynamic significance of the stenosis. We then test whether administering adenosine significantly improves diagnostic performance of iFR. Methods In 51 vessels intra-coronary pressure and flow velocity was measured distal to the stenosis at rest and during adenosine mediated hyperaemia. iFR (at rest and during adenosine administration, iFRa), FFR, HSR, baseline and hyperaemic microvascular resistance were calculated using automated algorithms. Results iFRa had significantly lower values than FFR and iFR (median iFRa 0.73 (0.58, 0.85) versus median FFR 0.84 (0.70, 0.89) and median iFR 0.93 (0.83, 0.98) p<0.001 for both). Despite this, differences in magnitude of microvascular resistance between indices did not significantly alter diagnostic agreement with HSR (ROC AUC: iFR 0.93 vs iFRa 0.94 and FFR 0.96, p=0.45). When iFR and FFR disagreed (4 cases, 7.7% of the study population), HSR agreed with iFR in 50% of cases and with FFR in 50% of cases. Conclusion iFR and FFR had equivalent agreement with classification of coronary stenosis severity by HSR. Further reduction in resistance by the administration of adenosine did not improve diagnostic categorisation. This suggests that basal iFR flow is sufficient to allow accurate discrimination of stenosis severity.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".