Bibliographic record
Abstract
The pattern of breathing during a hyperthermia-induced hyperventilation varies across different species. Thermal tachypnea is a first phase panting response adopted during hyperthermia when tidal volume is minimized and the frequency of breathing is maximized. Blood-gas tensions and pH are maintained during this hyperventilation, and the associated heat loss helps the animal regulate its body temperature. A second pattern of breathing adopted in hyperthermia is thermal hyperpnea; this response is the focus of this review. This form of hyperventilation is evident after an increase in core temperature and it is apparent in humans. Increases of tidal volume as well as frequency of breathing are evident during this response that results in a respiratory alkalosis. The cause of thermal hyperpnea is not resolved; evidence of the potential mechanisms underlying this response support that modulators of the response act in either a multiplicative or additive manner with body temperatures. The details of the designs and methodologies of the studies supporting or refuting these two views are discussed. A physiological rationale for thermal hyperpnea is presented in which it is suggested this response serves a heat-loss role and contributes to selective brain cooling in hyperthermic humans. Ongoing research in this area is focused on resolving the mechanisms underlying thermal hyperpnea and its contribution to cranial thermoregulation. The direct application of this research is for the care of febrile and hyperthermic patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".