P.094 Characterizing post-stroke autonomic functioning. sub-study protocol of the clinical arm of PARADISE study
Bibliographic record
Abstract
Background: Strokes can cause a variety of cardiovascular complications. The underlying mechanisms are largely unknown but there is evidence that dysautonomia plays a role in stroke induced-heart injury (SIHI) and arrhythmias triggered by damage of specific brain regions involved in the autonomic regulation of cardiac functions. Understanding these mechanisms could aid in preventing these cardiovascular consequences. We hypothesize that compared to -patients with sinus rhythm (SR) or with cardiogenic atrial fibrillation known before the stroke (cKAF) or diagnosed after the stroke (cAFDAS), those with neurogenic AFDAS (nAFDAS) will show a specific pattern of autonomic functioning. Methods: We will prospectively evaluate 200 ischemic stroke patients at the London Health Sciences Center University Hospital. Participants will undergo continuous electrocardiographic monitoring during 14 days. Based on pre-specified criteria, patients with AFDAS will be classified into presumably neurogenic vs cardiogenic. We will assess autonomic function within 14 days after stroke onset by using the Autonomic Reflex Screening. We will compare markers of cardiac dysfunction with autonomic changes, as well as specific stroke topographies for SR, cKAF, cAFDAS, and nAFDAS. Results: We are currently performing an interim analysis. Conclusions: Characterizing the autonomic changes that occur after ischemic stroke and their relationship with heart injury will help to advance knowledge on the pathophysiology of SIHI.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".