Causality detection in cardio-postural interaction under orthostatic stress induced by quiet standing using transfer entropy
Bibliographic record
Abstract
Representative signals of the cardiovascular and postural systems are known to interact with each other under orthostatic stress to maintain homeostasis, however, causal interaction between them is still unknown. In this research the mathematical framework of transfer entropy was applied to explore the existence of cause and effect relationship of anterior-posterior (COPy), medio-lateral (COPx) and resultant body sway (COPr) with blood pressure waveform (BPW) and calf muscle electromyogram (EMG). Simultaneous blood pressure, calf EMG, COPx, COPy and COPr data were acquired from 5 healthy young subjects during a 5-minute sit to stand test. Data from the last 4 minutes in the stand phase was analyzed for causality between the signal pairs. A moving time window of 15 second length with a 5 second overlap between successive windows resulted in a total of 23 estimates of causality. The results indicate the existence of a causal interaction between the variables in cardio-postural interactions. The BPW showed dominant causal coupling with COPx, COPy and COPr. Additionally, EMG showed dominant causal coupling with COPy and COPr, with COPx similar amount of bidirectional causality was observed. The results strongly suggest the presence of a directional causal loop from the blood pressure control center playing a central role.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".