Right Atrial Collision Time (RACT): A Novel Marker Of Propensity For Typical Atrial Flutter
Bibliographic record
Abstract
Introduction: The risk of typical atrial flutter (AFL) is increased proportionately to right atrial (RA) size or right atrial scarring that results in reduced conduction velocity. These characteristics result in propagation of a flutter wave by ensuring the macro re-entrant wave front does not meet its refractory tail. The time taken to traverse the circuit would take account of both of these characteristics and may provide a novel marker of propensity to develop AFL. Our goal was to investigate right atrial collision time (RACT) as a marker of existing or future typical AFL. Methods: This single centre, prospective study recruited consecutive typical AFL ablation patients that were in sinus rhythm. Controls were consecutive electrophysiology study patients >18 years of age. While pacing the coronary sinus (CS) ostium at 600 ms, a local activation time map was created to locate the latest collision point on the anterolateral right atrial wall. This RACT is a measure of conduction velocity and distance from CS to a collision point on the lateral right atrial wall. Results: 98 patients were included in the analysis, 41 with atrial flutter and 57 controls. Patients with atrial flutter were older, 64.7 ± 9.7 vs 52.4 ± 16.8 years (<0.001) and more often male (34/41vs 31/57 (0.003)). The AFL group mean RACT (132.6±17.3 ms) was significantly longer than that of controls (99.1±11.6 ms) (p<0.001). A RACT cut-off of 115.5 ms had a sensitivity and specificity of 92.7% and 93.0% respectively for diagnosis of atrial flutter. An ROC curve indicated an AUC of 0.96 (95% CI: 0.93-1.0, p<0.01). Conclusion : RACT is a novel and promising marker of propensity for typical AFL. This data will inform larger prospective studies. The ability to predict AFL would be of significant clinical value to guide anticoagulation and ablation decisions.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".