Effect of transdermally administered fentanyl on minimum alveolar concentration of isoflurane in normothermic and hypothermic dogs
Bibliographic record
Abstract
OBJECTIVE: To determine whether the minimum alveolar concentration (MAC) of isoflurane was altered by transdermal administration of fentanyl in normothermic and hypothermic dogs. DESIGN: Randomized complete block crossover design. ANIMALS: 6 mature healthy dogs. PROCEDURE: Dogs received each of 4 treatments in random order. Following induction of anesthesia, normothermia was maintained in dogs that were treated with a fentanyl patch (F-NORM) or sham patch (C-NORM), or hypothermia was maintained in dogs that were treated with a fentanyl patch (F-HYPO) or sham patch (C-HYPO). The appropriate patch was applied 24 hours prior to induction of anesthesia. Anesthesia was induced with isoflurane in oxygen; the dogs were intubated and mechanically ventilated. Target esophageal temperatures were maintained within 1 degrees C of baseline values (normothermia) or at 34.5 degrees C (94.1 degrees F; hypothermia) for 1 hour prior to starting MAC determinations. Supramaximal stimulation was achieved with an electrical stimulator attached to needle electrodes placed in the buccal mucosa of the lower jaw of the dog. RESULTS: Mean MAC +/- SEM of isoflurane during C-NORM, C-HYPO, F-NORM, and F-HYPO treatments were 1.20 +/- 0.17, 0.89 +/- 0.18, 0.76 +/- 0.10, and 0.81 +/- 0.17, respectively. The mean MAC during C-NORM was significantly higher than values for the other treatments. There was no significant difference in mean MAC among the C-HYPO, F-NORM, and F-HYPO treatments. CONCLUSIONS AND CLINICAL RELEVANCE: Data suggest that transdermal administration of fentanyl significantly reduces isoflurane requirements in normothermic dogs. The isoflurane MAC-sparing effects of transdermal fentanyl are not apparent in hypothermic dogs.
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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 | 0.000 |
| 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".