Ectothermy and cardiac shunts profoundly slow the equilibration of inhaled anaesthetics in a multi-compartment model
Bibliographic record
Abstract
Abstract The use of inhalational anaesthesia is ubiquitous in terrestrial vertebrates. Given the dependence of these agents on delivery by the cardiorespiratory system, we developed a new computational model predicting equilibration of inhaled anaesthetics in mammalian and ectotherm conditions including the ability of reptiles to maintain vascular shunts. A multi-compartment model was constructed from simultaneously-solved equations, verified by comparison to the literature for endo and ectotherm physiology. The time to 90% equilibration of anaesthetic in arterial blood (t90) is predicted and used to compare anaesthetics and physiologies. The five to tenfold lower cardiac output and minute ventilation of ectothermic vertebrates is predicted to slow equilibration times by five to ten times leading to 90% equilibration in ectotherm arterial blood of over 200 min, compounded by reduction in body temperature, and the extent of right-to-left vascular shunts. The impact of these findings is also influenced by the solubility coefficient of the anaesthetic, such that at net right-to-left shunt fractions of over 0.8, sevoflurane loses the advantage of faster equilibration, in comparison with isoflurane. We explore clinical strategies to regulate anaesthetic uptake in ectotherms by managing convectional flow especially by supportive ventilation and reduction of the right-to-left shunt.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".