Wavelet transform cardiorespiratory coherence for monitoring nociception
Bibliographic record
Abstract
Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for nociception monitoring during general anesthesia. A novel wavelet transform cardiorespiratory coherence (WTCRC) algorithm was used to calculate estimates of the linear coupling between heart rate and respiration. WTCRC was tested on clinical data from 19 pediatric patients receiving general anesthesia during dental surgery. WTCRC decreased during nociception, and increased following additional anesthetic drugs. Data were divided into categories with normal respiratory rate (RR) (in the HF band) and low RR (in the LF band), then split into 2-minute windows. WTCRC and LF/HF were calculated for each window and compared in each category. The algorithms showed correlations of −0.5506 and −0.1403 for data with normal and low RR, respectively. WTCRC and LF/HF are comparable when the RR is normal, and WTCRC significantly outperforms when the RR is low.
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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 | 0.001 |
| 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".