Obesity prolongs induction times in reptiles
Bibliographic record
Abstract
Obesity is common in captive reptiles, and reptiles are increasingly popular as companion animals and in physiological research. Obesity may present a challenge during surgical procedures using inhalation anaesthesia, as the long induction time due to the low reptilian metabolism may increase anaesthetic accumulation in the adipose tissues. This study investigated the impact of obesity on induction and recovery times from inhaled anaesthesia. The temporal change in the partial pressure of isoflurane in different tissues was predicted using a multi-compartment model. Furthermore, as right-to-left shunting can delay anaesthetic uptake and washout, we included an assessment of the combination of cardiac shunting and obesity. The model predictions indicate a clear increase in time to reach 90% equilibration of administered anaesthetic in the brain (T90) of obese non-shunting (lean 47 min, obese >100 min) and shunting (lean 81 min, obese >100 min) reptiles. The combination of obesity and shunting doubled the time to acquisition of mean anaesthetic concentration (a measure used to plan anaesthesia) from 8 min to 19 min. Adipose blood flow highly affected whether the body type had an impact on induction time, with low adipose blood flow abolishing the effect of body type. As T90 was never reached within 100 min with both the obese reptiles, it was not possible to conclude on the effect of obesity on recovery times within this study. Care should therefore be taken when anaesthetising obese reptiles for surgical purposes, to ensure adequate anaesthetic depth is attained, and recovery monitored closely.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".