Fifty years of microneurography: learning the language of the peripheral sympathetic nervous system in humans
Bibliographic record
Abstract
As a primary component of homeostasis, the sympathetic nervous system enables rapid adjustments to stress through its ability to communicate messages among organs and cause targeted and graded end organ responses. Key in this communication model is the pattern of neural signals emanating from the central to peripheral components of the sympathetic nervous system. But what is the communication strategy employed in peripheral sympathetic nerve activity (SNA)? Can we develop and interpret the system of coding in SNA that improves our understanding of the neural control of the circulation? In 1968, Hagbarth and Vallbo (Hagbarth KE, Vallbo AB. Acta Physiol Scand 74: 96-108, 1968) reported the first use of microneurographic methods to record sympathetic discharges in peripheral nerves of conscious humans, allowing quantification of SNA at rest and sympathetic responsiveness to physiological stressors in health and disease. This technique also has enabled a growing investigation into the coding patterns within, and cardiovascular outcomes associated with, postganglionic SNA. This review outlines how results obtained by microneurographic means have improved our understanding of SNA outflow patterns at the action potential level, focusing on SNA directed toward skeletal muscle in conscious humans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".