A method for calculating the gas volume proportions and inhalation temperature of inert gas mixtures allowing reaching normothermic or hypothermic target body temperature in the awake rat
Bibliographic record
Abstract
The noble gases xenon (Xe) and helium (He) are known to possess neuroprotective properties. Xe is considered the golden standard neuroprotective gas. However, Xe has a higher molecular weight and lower thermal conductivity and specific heat than those of nitrogen, the main diluent of oxygen (O2) in air, conditions that could impair or at least reduce the intrinsic neuroprotective properties of Xe by increasing the critical care patient's respiratory workload and body temperature. In contrast, He has a lower molecular weight and higher thermal conductivity and specific heat than those of nitrogen, but is unfortunately far less potent than Xe at providing neuroprotection. Therefore, combining Xe with He could allow obtaining, depending on the gas inhalation temperature and composition, gas mixtures with neutral or hypothermic properties, the latter being advantageous in term of neuroprotection. However, calculating the thermal properties of a mixture, whatever the substances – gases, metals, rubbers, etc. – is not trivial. To answer this question, we provide a graphical method to assess the volume proportions of Xe, He and O2 that a gas mixture should contain, and the inhalation temperature to which it should be administered to allow a clinician to maintain the patient at a target body temperature.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".