Quantifying sympathetic nerve activity: problems, pitfalls and the need for standardization
Bibliographic record
Abstract
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.
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 | 0.031 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".