European Stroke Organisation (ESO) guideline on screening for subclinical atrial fibrillation after stroke or transient ischaemic attack of undetermined origin
Bibliographic record
Abstract
We aimed to provide practical recommendations for the screening of subclinical atrial fibrillation (AF) in patients with ischaemic stroke or transient ischaemic attack (TIA) of undetermined origin. These guidelines are based on the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. Five relevant Population, Intervention, Comparator, Outcome questions were defined by a multidisciplinary module working group (MWG). Longer duration of cardiac rhythm monitoring increases the detection of subclinical AF, but the optimal monitoring length is yet to be defined. We advise longer monitoring to increase the rate of anticoagulation, but whether longer monitoring improves clinical outcomes needs to be addressed. AF detection does not differ from in- or out-patient ECG-monitoring with similar monitoring duration, so we consider it reasonable to initiate in-hospital monitoring as soon as possible and continue with outpatient monitoring for more than 48 h. Although insertable loop recorders (ILR) increase AF detection based on their longer monitoring duration, comparison with non-implantable ECG devices for similar monitoring time is lacking. We suggest the use of implantable devices, if feasible, for AF detection instead of non-implantable devices to increase the detection of subclinical AF. There is weak evidence of a useful role for blood, ECG and brain imaging biomarkers for the identification of patients at high risk of AF. In patients with patent foramen ovale, we found insufficient evidence from RCT, but prolonged cardiac monitoring in patients >55 years is advisable for subclinical AF detection. To conclude, in adult patients with ischaemic stroke or TIA of undetermined origin, we recommend longer duration of cardiac rhythm monitoring of more than 48 h and if feasible with IRL to increase the detection of subclinical 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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".