A signature of fetal systemic inflammatory response in the pattern of heart rate variability measures matrix: a prospective study in fetal sheep model of lipopolysaccharide (LPS)‐induced sepsis
Bibliographic record
Abstract
Fetal cholinergic anti‐inflammatory pathway (CAP) provides negative feedback on systemic inflammation. This is reflected in subtle alterations of fetal heart rate (FHR) variability (fHRV). We hypothesized that distinct patterns of fHRV correlation to pro‐inflammatory cytokines will reflect CAP's spontaneous versus inflammatory response states. We induced variable degrees of inflammatory response with LPS in chronically instrumented near‐term fetal sheep (n=8). CAP activity was quantified by 99 fHRV measures using CIMVA (continuous individualized multivariate variability analysis). We correlated the time‐matched fHRV and TNF‐α, IL‐1β and IL‐6. IL‐6, but not TNF‐α and IL‐1β, peaked at 3 hours. At baseline, a distinct set of seven fHRV measures from invariant, geometric and statistical domains correlated to cytokines. During fetal inflammatory response state, another subset of thirteen fHRV measures from invariant, geometric, statistical and energetic domains correlated to these cytokines. Distinctive subsets of fHRV measures can be identified that correlate with levels of inflammation. This opens a new way to study fetal inflammatory response as a function of neuroimmunological behaviour reflected in patterns of fHRV. Funded by CIHR, FRSQ, MITACS/NeuroDevNet, Molly Towell.
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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 | 0.001 |
| 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".