Heart rate variability and functional outcome in ischemic stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Heart rate variability (HRV) as a measure of autonomic function might provide prognostic information in ischemic stroke. However, numerous difficulties are associated with HRV parameters assessment and interpretation, especially in short-term ECG recordings. For better understanding of derived HRV data and to avoid methodological bias we simultaneously recorded and analyzed heart rate, blood pressure and respiratory rate. METHODS: Seventy-five ischemic stroke patients underwent short-term ECG recordings. Linear and nonlinear parameters of HRV as well as beat-to-beat blood pressure and respiratory rate were assessed and compared in patients with different functional neurological outcomes at 7th and 90th days. RESULTS: Values of Approximate, Sample and Fuzzy Entropy were significantly lower in patients with poor early neurological outcome. Patients with poor 90-day outcome had higher percentage of high frequency spectrum and normalized high frequency power, lower normalized low frequency power and lower low frequency/high frequency ratio. Low frequency/high frequency ratio correlated negatively with scores in the National Institutes of Health Stroke Scale and modified Rankin Scale (mRS) at the 7th and mRS at the 90th days. Mean RR interval, values of blood pressure as well as blood pressure variability did not differ between groups with good and poor outcomes. Respiratory frequency was significantly correlated with the functional neurological outcome at 7th and 90th days. CONCLUSION: While HRV assessed by linear methods seems to have long-term prognostic value, complexity measures of HRV reflect the impact of the neurological state on distinct, temporary properties of heart rate dynamic. Respiratory rate during the first days of the stroke is associated with early and long-term neurological outcome and should be further investigated as a potential risk factor.
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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 | 0.000 | 0.003 |
| 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".