Abstract TP201: How Often Is Occult Atrial Fibrillation In Cryptogenic Stroke Causal Versus Incidental?
Bibliographic record
Abstract
Introduction: Long-term cardiac monitoring studies have unveiled low-burden, occult atrial fibrillation (AF) in some patients with otherwise cryptogenic stroke (CS). But occult AF is also found in some normal individuals and patients with stroke of known cause (KS). Clinical management would be aided by estimates of how often occult AF in a patient with CS is causal versus incidental. Methods: Through systematic search, we identified all case-control and cohort studies applying identical long-term monitoring techniques to both CS and KS patients. We performed a random-effects meta-analyses across these studies to determine the best estimate of the differential frequency of occult AF in CS and KS among all patients, and across age subgroups. We then applied Bayes’ theorem (method of Alsheikh-Ali et al, Stroke 2009) to determine the probability that occult AF is causal or incidental. Results: The systematic search identified 3 case-control and cohort studies enrolling 575 patients (337 CS, 238 KS). Methods of long-term monitoring were implantable loop recorder in 191 (33.2%), extended mobile external cardiac monitor in 277 (48.2%), and patch/event/Holter monitoring in 107 (18.6%). In formal meta-analysis, the summary odds ratio for occult AF in CS vs KS in all patients was 1.72 (95%CI 0.91-3.25) (Figure). With application of Bayes theorem, the corresponding probabilities indicated that, when present, occult AF in CS patients is causal in 44.4% and incidental in 55.6%. Only one study reported age-specific subgroup data: these suggested occult AF in CS patients was causal in 93% of patients <65y vs 5.6% of patients ≥65y, but estimates had limited precision. Conclusions: In patients with cryptogenic stroke, when occult AF is found, it is likely causal in about 44% of patients, who may benefit from anticoagulation, and likely incidental in about 56% of patients, who may not benefit from anticoagulation. Figure
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 | 0.030 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.017 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".