Effects of acepromazine, hydromorphone, or an acepromazine-hydromorphone combination on the degree of sedation in clinically normal dogs
Bibliographic record
Abstract
OBJECTIVE: To determine the effects of IM administration of acepromazine, hydromorphone, or the acepromazine-hydromorphone combination on degree of sedation in clinically normal dogs and to compare 2 sedation scoring techniques. DESIGN: Prospective, randomized, blinded, controlled trial. Animals-46 random-source dogs. PROCEDURES: Dogs were assigned to receive IM administrations of acepromazine (0.05 mg/kg [0.023 mg/lb]; [DOSAGE ERROR CORRECTED] n = 12), hydromorphone (0.1 mg/kg [0.045 mg/lb]; 11), acepromazine-hydromorphone (0.5 mg/kg and 0.1 mg/kg, respectively; 12), or saline (0.9% NaCI) solution (0.05 mL/kg [0.023 mL/lb]; 11). Sedation scores were determined at 0 (time of administration), 15, 30, 45, and 60 minutes by use of a subjective scoring system (SSS) and a simple numeric rating scale (NRS). RESULTS: Acepromazine caused significantly greater sedation than did saline solution at 15, 30, 45, and 60 minutes. Acepromazine-hydromorphone caused significantly greater sedation than did saline solution at 15, 30, 45, and 60 minutes and than did hydromorphone alone at 30 minutes. Hydromorphone alone did not cause significantly greater sedation than did saline solution. All treatments, including saline solution, caused significantly greater sedation at 45 and 60 minutes, compared with sedation at time 0. There was a significant correlation (r(2) = 0.72) between scores obtained with the SSS and NRS, but the NRS was less sensitive for detecting clinically important sedation. CONCLUSIONS AND CLINICAL RELEVANCE: Administration of acepromazine or acepromazine-hydromorphone caused sedation in clinically normal dogs, whereas administration of hydromorphone alone did not. The NRS was a less-reliable measure of sedation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".