A comparison of alfaxalone and propofol on intraocular pressure in healthy dogs
Bibliographic record
Abstract
OBJECTIVE: To compare the effects of alfaxalone and propofol on intraocular (IOP) pressure in the canine eye. ANIMALS STUDIED: Twenty-three healthy adult dogs. PROCEDURES: Dogs were randomized to receive intravenous propofol (n = 11) or alfaxalone (n = 12) until loss of jaw tone, 20 min after intravenous premedication (acepromazine 0.02-0.03 mg/kg and hydromorphone 0.05-0.1 mg/kg). IOP was measured at baseline (BL), 20 min postpremedication (postpremed), loss of jaw tone (postinduct), and immediately following orotracheal intubation (postintub). Between- and within-treatment effects were analyzed with two-way and one-way repeated measures ANOVA with Bonferroni's post hoc test, respectively. P < 0.05 was considered significant. RESULTS: No significant IOP differences were detected between alfaxalone or propofol groups at any time point (P > 0.05). Propofol: IOP did not change between BL (15.5 ± 2.7 mmHg) and postpremed (16.2 ± 3.6 mmHg, P > 0.05), or postinduct (19.1 ± 5.2 mmHg) and postintub (21.0 ± 4.6 mmHg, P > 0.05), but differed significantly between BL and postinduct (P < 0.0001), and postintub (P < 0.0001). Alfaxalone: IOP did not change between BL (15.7 ± 2.8 mmHg) and postpremed (15.3 ± 4.1 mmHg, P > 0.05), or postinduct (19.2 ± 4.9 mmHg) and postintub (20.5 ± 4.5 mmHg, P > 0.05), but differed significantly between BL and postinduct (P < 0.01), and postintub (P < 0.0001). CONCLUSIONS: These data show a potentially clinically significant increase in IOP following induction with propofol or alfaxalone, but no difference between agents.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".