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Quantifying sympathetic nerve activity: problems, pitfalls and the need for standardization

2009· review· en· W1561501729 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

VenueExperimental Physiology · 2009
Typereview
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsMemorial University of Newfoundland
FundersHealth Research Council of New ZealandMedical Research CouncilFondation pour la Recherche MédicaleAuckland Medical Research Foundation
KeywordsStandardizationNormalization (sociology)Computer scienceMedicineData mining

Abstract

fetched live from OpenAlex

Since the first recording of sympathetic nerve activity (SNA) early last century, numerous methods for presentation of the resulting data have developed. In this paper, we discuss the common ways of describing SNA and their application to chronic recordings. Suggestions on assessing the quality of SNA are made, including the use of arterial pressure wave-triggered averages and nasopharyngeal stimuli. Calculation of the zero level of the SNA signal from recordings during ganglionic blockade, the average level between bursts and the minimum of arterial pressure wave-triggered averages are compared and shown to be equivalent. The use of normalization between zero and maximal SNA levels to allow comparison between groups is discussed. We recommend that measured microvolt levels of integrated SNA be presented (with the zero/noise level subtracted), along with burst amplitude and frequency information whenever possible. We propose that standardization of the quantifying/reporting of SNA will allow better comparison between disease models and between research groups and ultimately allow data to be more reflective of the human situation.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.073
GPT teacher head0.375
Teacher spread0.302 · 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