Effect of remifentanil hydrochloride administered via constant rate infusion on the minimum alveolar concentration of isoflurane in cats
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of increasing doses of remifentanil hydrochloride administered via constant rate infusion (CRI) on the minimum alveolar concentration (MAC) of isoflurane in cats. ANIMALS: 6 healthy adult cats. PROCEDURES: For each cat, 2 experiments were performed (2-week interval). On each study day, anesthesia was induced and maintained with isoflurane; a catheter was placed in a cephalic vein for the administration of lactated Ringer's solution or remifentanil CRIs, and a catheter was placed in the jugular vein for collection of blood samples for blood gas analyses. On the first study day, individual basal MAC (MAC(Basal)) was determined for each cat. On the second study day, 3 remifentanil CRIs (0.25, 0.5, and 1.0 microg/kg/min) were administered (in ascending order); for each infusion, at least 30 minutes elapsed before determination of MAC (designated as MAC(R0.25), MAC(R0.5), and MAC(R1.0), respectively). A 15-minute washout period was allowed between CRIs. A control MAC (MAC(Control)) was determined after the last remifentanil infusion. RESULTS: Mean +/- SD MAC(Basal) and MAC(Control) values at sea level did not differ significantly (1.66 +/- 0.08% and 1.52 +/- 0.21%, respectively). The MAC values determined for each remifentanil CRI did not differ significantly. However, MAC(R0.25), MAC(R0.5), and MAC(R1.0) were significantly decreased, compared with MAC(Basal), by 23.4 +/- 7.9%, 29.8 +/- 8.3%, and 26.0 +/- 9.4%, respectively. CONCLUSIONS AND CLINICAL RELEVANCE: The 3 doses of remifentanil administered via CRI resulted in a similar degree of isoflurane MAC reduction in adult cats, indicating that a ceiling effect was achieved following administration of the lowest dose.
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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 | 0.000 |
| 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".