Revisiting the evaluation of central versus peripheral thermoregulatory control in humans
Bibliographic record
Abstract
Human thermoregulatory control is often evaluated through the relationship between thermoeffector output and core or mean body temperature. In addition to providing a general indication of whether a variable of interest alters thermoregulatory control, this relationship is often used to determine how this alteration may occur. This latter interpretation relies upon two parameters of the thermoeffector output-body temperature relationship: the onset threshold and thermosensitivity. Traditionally, changes in the onset threshold and thermosensitivity are interpreted as "central" or "peripheral" modulation of thermoregulatory control, respectively. This mini-review revisits the origins of the thermoeffector output-body temperature relationship and its use to interpret "central" or "peripheral" modulation of thermoregulatory control. Against this background, we discuss the strengths and weaknesses of this approach and highlight that "central" thermoregulatory control reflects the neural control of body temperature whereas "peripheral" thermoregulatory control reflects properties specific to the thermoeffector organs. We highlight studies that employed more direct approaches to investigate the neural control of body temperature and peripheral properties of thermoeffector organs. We conclude by encouraging future investigations interested in studying thermoregulatory control to more directly investigate the component of the thermoeffector loop under investigation.heat; human; skin blood flow; sweat; thermoregulatory.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".