Embolic Stroke of Undetermined Source: Role of Implantable Loop Recorder in Secondary Prevention
Bibliographic record
Abstract
BACKGROUND AND AIMS: Atrial fibrillation (AF) is the most important cause of embolic stroke of undetermined source (ESUS). Implantable loop recorder (ILR) demonstrated the highest sensitivity for detecting it. This register was created to confirm the high prevalence of AF in patients after ESUS and to verify possible benefits on clinical outcomes such as TIA (Transient Ischaemic Attack)/stroke recurrence and death using ILR. METHODS: A total of 278 patients admitted to "Molinette" Hospital in Stroke Unit department between 2011 and 2016, diagnosed with ESUS, underwent ILR implantation if they had at least one risk factor for AF. A total of 165 patients admitted to other departments in the same center for the same pathology, without ILR, represent the control group. We used propensity score to select 132 patients from each group (matching age, sex, CHADS-VASC, and HAS-BLEED baseline characteristics). RESULTS: The detection rate of AF episodes was significantly higher in the ILR group (p < 0.001). No significant protective role of ILR for clinical endpoints was found on univariate analysis, although a trend towards significance has been pointed for the composite outcome of death and ischemic events recurrence (OR 0.52, CI 0.26-1.04, p = 0.06). A protective role of ILR was found for deaths (OR 0.4, CI 0.17-0.94, p 0.03) and for the composite outcome (OR 0.41, CI 0.19-0.87, p 0.02) on multivariate analysis in the best subsets. CONCLUSION: With our statistical models, we identified a significant clinical benefit from ILR monitoring, evidenced by a trend of less death and TIA/stroke recurrence and relevant ILR protection for prediction of TIA/stroke recurrence.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".