Abstract TP31: Atrial Cardiopathy: Incidence in Endovascular Thrombectomy Patients
Bibliographic record
Abstract
Background: Emerging evidence suggests that underlying atrial cardiopathy (AC) may result in thromboembolism formation in the absence of atrial fibrillation (AF). This may explain a proportion of large vessel occlusion (LVO) cryptogenic strokes. The prevalence of AC in endovascular thrombectomy (EVT) patients has not been assessed. Methods: A prospectively maintained database of EVT patients treated at a comprehensive stroke centres between January 2016 and September 2018 was retrospectively screened. Patients undergoing EVT for acute ischemic stroke with admission electrocardiogram (ECG) were selected. Subjects were screened for AF, paroxysmal AF (pAF) and AC with previously validated ECG markers (P-wave terminal force in lead V1 - PTFV1 >4000μV/ms & prolonged P-wave duration - PWD >120 ms. Results: A total of 189 patients were included. Atrial fibrillation was present in 73 (38.6%) patients. Paroxysmal AF was recorded in 31 (16.4%) patients. Atrial cardiopathy markers were present in 88 (46.6%) of the total cohort, compared to 7.7% in a published general population reference (p < 0.001). Atrial cardiopathy was present in 23 (74%) of pAF patients. Conclusion: Atrial cardiopathy occurs frequently in EVT patients, suggesting it may be a LVO stroke risk factor. Atrial cardiopathy may be associated with pAF. Further studies in this patient population are recommended.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".