Real-time cardiorespiratory coherence detects antinociception during general anesthesia
Bibliographic record
Abstract
Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for monitoring nociception during general anesthesia. A novel real-time cardiorespiratory coherence (CRC) algorithm has been developed to analyze the strength of linear coupling between heart rate (HR) and respiration. CRC values range from 0 (low coherence, strong nociception) to 1 (high coherence, no nociception). The algorithm uses specially designed filters to operate in real-time, minimizing computational complexity and time delay. In the standard HRV high frequency band of 0.15 - 0.4 Hz, the real-time delay is only 5.25 - 3.25 s. We have assessed the algorithm's response to 60 anesthetic bolus events (a large dose of anesthetics given over a short time; strongly antinociceptive) recorded in 47 pediatric patients receiving general anesthesia. Real-time CRC responded strongly to bolus events, changing by an average of 30%. For comparison, three traditional measures of HRV (LF/HF ratio, SDNN, and RMSSD) responded on average by only 3.8%, 14%, and 3.9%, respectively. Finally, two traditional clinical measures of nociception (HR and blood pressure) responded on average by only 3.9% and 0.91%, respectively. CRC may thus be used as a real-time nociception monitor during general anesthesia.
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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".