Development and Testing of a Sedation Scale for Use in Rabbits (<i>Oryctolagus cuniculus</i>)
Bibliographic record
Abstract
In biomedical research, rabbits are commonly sedated to facilitate a variety of procedures. Developing a sedation assessment scale enables standardization of levels of sedation and comparisons of sedation protocols, and may help in predicting sedation level requirements for different procedures. The goal of this study was to develop a rabbit sedation assessment scale using a psychometric approach. We hypothesized that the sedation scale would have construct validity, good internal consistency, and reliability. In a prospective, randomized, blinded study design, 15 (8 females, 7 males) healthy 1-y-old New Zealand white rabbits received 3 intramuscular treatments: midazolam (0.5 mg/kg; n = 6); midazolam (1.5 mg/kg)–ketamine (5 mg/kg; n = 7); and alfaxalone (4 mg/kg)–dexmedetomidine (0.1 mg/kg)–midazolam (0.2 mg/kg; n = 3). One rabbit received 2 treatments. A sedation scale was developed by using psychometric methods, with assessment performed by 6 independent raters who were blind to treatment. Final sedation scale items included posture, palpebral reflex, orbital tightening, lateral recumbency, loss of righting reflex, supraglottic airway device placement, toe pinch, and general appearance. The scale showed construct validity, good to very good interrater reliability for individual items (6 raters; intraclass correlation coefficient, 0.671 to 0.940), very good intrarater reliability (5 raters; intraclass correlation coefficient, 0.951 to 0.987), and excellent internal consistency (Cronbach α, 0.947). The sedation scale performed well under the conditions tested, suggesting that it can be applied in a wider range of settings (different populations, raters, sedation protocols).
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".