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Record W2104868957

Wavelet transform cardiorespiratory coherence for monitoring nociception

2010· article· en· W2104868957 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputing in Cardiology · 2010
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsCardiorespiratory fitnessNociceptionRespiratory rateAnesthesiaHeart rateHeart rate variabilityRespiratory frequencyElectrocardiographyMedicineAnestheticCoherence (philosophical gambling strategy)Wavelet transformWaveletRespiratory systemBiomedical engineeringMathematicsCardiologyInternal medicineComputer scienceArtificial intelligenceBlood pressureStatisticsReceptor
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.308
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it