Nonlinear properties of vagal and sympathetic modulations of heart rate variability in ovine fetus near term
Bibliographic record
Abstract
Fetal heart rate (FHR) monitoring is commonly used although clinical studies questioned its diagnostic value. Sophisticated FHR variability (fHRV) measures such as fHRV complexity may improve the sensitivity and specificity of FHR monitoring. A more detailed understanding of the physiology underlying fHRV complexity is essential to harness its use for monitoring fetal health. To examine the specific effects of vagal and sympathetic modulations on fHRV complexity, we blocked vagal activity with atropine and sympathetic activity with propranolol in near-term fetal sheep (n = 7, 0.85 gestation). Under these conditions, we analyzed the linear and nonlinear parts of fHRV complexity from autonomic information flow. Overall fHRV complexity decreased with both drugs compared with nonrapid eye movement sleep baseline (P < 0.05). With atropine, this was because of a decrease of the linear part of fHRV complexity on the long-term time scale (P < 0.05), suggesting that vagal modulation of fHRV is adequately described by linear fHRV measures. With propranolol, the nonlinear part of fHRV complexity decreased on the short-term time scale (P < 0.05), suggesting that sympathetic influences on fHRV can be detected by the nonlinear part of fHRV complexity. Thus the complex interplay of vagal and sympathetic modulations of fHRV is reflected differently and specifically in the linear and nonlinear properties of fHRV complexity, and on different time scales. Analysis of linear and nonlinear properties of fHRV may improve sensitivity and specificity of FHR monitoring.
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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 | 0.001 |
| 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".