Abstract WP110: Infarct Topography On MRI In Patients With Acute Ischemic Stroke And Atrial Fibrillation: Subgroup Analysis From PER DIEM Trial
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is a common cause of ischemic stroke; however, it is often difficult to detect. It is unclear whether specific infarct topography on magnetic resonance imaging (MRI) is associated with underlying AF. We aimed to objectively assess the infarct patterns on MRI in patients with acute ischemic stroke and determine imaging characteristics that are associated with AF. Methods: We conducted a subgroup analysis on patients randomized in Post-Embolic Rhythm Detection with Implantable vs External Monitoring trial (PER DIEM; NCT02428140) who had brain MRI. Two raters blinded to clinical details reviewed the MRI findings. Patients were divided to two groups (AF and non-AF) and descriptive statistics were used to characterize findings. Variables associated with new AF were analyzed using logistic regression and reported as odds ratios (OR) with 95% confidence interval (CI) and p -values. Results: Of the 300 patients who were randomized in the trial, 249 (83%) patients (59.4% male) with a mean age of 64.3 ± 13.1 years had MRI brain and were included in the analysis. Median (IQR) NIHSS was 0 (0 - 1), number of lesions was 2 (1 - 3), and diameter of lesion (mm) was 10.4 (5.8 - 21.1) mm. In this cohort of patients, imaging characteristics were not significantly associated with the detection of AF. Conclusions: Association between infarct topography and AF detection was not found in this study. Imaging characteristics cannot be relied upon to predict or exclude an underlying AF. Large prospective studies are suggested to examine the link between infarct topography and underlying AF.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".