Autonomic Parameter and Stress Profile Predict Secondary Ischemic Events After Transient Ischemic Attack or Minor Stroke
Bibliographic record
Abstract
Background and Purpose- Traditional risk factors for ischemic stroke are body stressors that are related to autonomic autonomic system (ANS) dysfunction. The value of ABCD2 score (age, blood pressure, clinical features, duration of symptoms, diabetes) to predict ischemic stroke after transient ischemic attack is compromised by the inclusion of a limited number of stressors. We aimed to assess whether markers of ANS function and stress could predict the occurrence of secondary ischemic events after transient ischemic attack or minor stroke. Methods- This is a prospective cohort study in which 201 patients were recruited within 48 hours after initial transient ischemic attack or minor stroke and followed for 90 days to assess the development of secondary ischemic events. ABCD2 score, heart rate variability (HRV) parameters as markers of ANS function, and psychological stress were assessed. Logistic regression and area under the curve (AUC) were used to assess the models' predictive ability. Results- Morning high frequency (HF) HRV power and changes in HF HRV from morning to afternoon (daytime HF changes) were the most useful HRV predictors for both ischemic events (AUC=0.61 and 0.70) and ischemic stroke (AUC=0.62 and 0.72). Compared with ABCD2 score, 2 HRV-based stress models showed higher predictive ability for ischemic events (AUC=0.82 versus 0.63, 0.76 versus 0.63; P<0.05) and ischemic stroke (AUC=0.87 versus 0.64, 0.82 versus 0.64; P<0.05). Conclusions- Assessing the effects of stress on the ANS may be an innovative way to stratify the risk of ischemic events after transient ischemic attack or minor stroke. New risk stratification by assessing the dynamic features of ANS dysfunction and stress may help identify high-risk sub-populations that may benefit from added management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".