Influence of prior determination of baseline minimum alveolar concentration (MAC) of isoflurane on the effect of ketamine on MAC in dogs.
Bibliographic record
Abstract
The objective of this study was to determine if prior measurement of the minimum alveolar concentration (MAC) of isoflurane influences the effect of ketamine on the MAC of isoflurane in dogs. Eight mixed-breed dogs were studied on 2 occasions. Anesthesia was induced and maintained using isoflurane. In group 1 the effect of ketamine on isoflurane MAC was determined after initially finding the baseline isoflurane MAC. In group 2, the effect of ketamine on isoflurane MAC was determined without previous measure of the baseline isoflurane MAC. In both groups, MAC was determined again 30 min after stopping the CRI of ketamine. Plasma ketamine concentrations were measured during MAC determinations. In group 1, baseline MAC (mean ± SD: 1.18 ± 0.14%) was decreased by ketamine (0.88 ± 0.14%; P < 0.05). The MAC after stopping ketamine was similar (1.09 ± 0.16%) to baseline MAC and higher than with ketamine (P < 0.05). In group 2, the MAC with ketamine (0.79 ± 0.11%) was also increased after stopping ketamine (1.10 ± 0.17%; P < 0.05). The MAC values with ketamine were different between groups (P < 0.05). Ketamine plasma concentrations were similar between groups during the events of MAC determination. The MAC of isoflurane during the CRI of ketamine yielded different results when methods of same day (group-1) versus separate days (group-2) are used, despite similar plasma ketamine concentrations with both methods. However, because the magnitude of this difference was less than 10%, either method of determining MAC is deemed acceptable for research purposes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".