Effects of different rates of propofol with or without S-ketamine on ventricular function in healthy cats – a randomized study
Bibliographic record
Abstract
Propofol is used for anesthetic induction in cats and procedural sedation in countries where alfaxalone is not available. Studies have reported propofol-related effects in echocardiography variables in dogs and humans. However, there is a lack of echocardiography studies investigating propofol-related effects on cats. This study aimed to use echocardiography to investigate echocardiographic changes in three protocols using propofol: propofol-slow (2 mg/kg/min, PS); propofol-fast (8 mg/kg/min, PF); propofol-ketamine (S-ketamine 2 mg/kg bolus followed by propofol 2 mg/kg/min; PK) in healthy premedicated (gabapentin-buprenorphine-acepromazine; 200 mg/cat, 0.4, and 0.1 mg/kg, respectively), non-intubated cats. Echocardiographic measurements were obtained at three time points: baseline (before the administration of propofol), end of propofol titration (end-point, T0), and 15 min after T0 (T15). Propofol at a lower rate continued from T0 to T15. Echocardiographic and physiological variables included fractional shortening (FS%), ejection fraction (EF%), HR, BP, and others. Propofol requirements at T0 for PF, PS, and PK groups were 5.0 ± 0.9, 3.8 ± 0.7, and 2.4 ± 0.5 mg/kg, respectively. EF% neither change over time nor between groups. PF and PK showed a reduction in FS% at T0 (47 ± 6 to 34 ± 6 and 42 ± 6 to 36 ± 5, respectively). BP reduced significantly in PF and PS groups (136 ± 26 to 105 ± 13 and 137 ± 22 to 115 ± 15 mmHg, respectively). It is unclear whether changes in echocardiography variables were of clinical relevance related to treatment groups or a result of within-group individual responses.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".