Intravenous chloral hydrate anesthesia provides appropriate analgesia for surgical interventions in male Sprague-Dawley rats
Bibliographic record
Abstract
BACKGROUND: The use of chloral hydrate as a sole maintenance anesthetic agent in rodent research has been controversial due to statements made in reference literature conflicting with results of primary research studies regarding its analgesic efficacy, and because of its associated tissue damage when administered intraperitoneally. OBJECTIVE: Our aim was to assess the analgesic efficacy of chloral hydrate using an intravenous (i.v.) route of administration, in order to prevent the local tissue irritation or ileus that has been previously reported using intraperitoneal (i.p.) routes. METHODS: We measured tail withdrawal latencies to a nociceptive thermal stimulus (infrared beam) in Sprague-Dawley rats-first when awake (unanesthetized), and then subsequently during i.v. chloral hydrate anesthesia. During anesthesia we also measured ongoing heart and respiration rates. RESULTS: Withdrawal latencies during chloral hydrate anesthesia were significantly higher, and often maximal, indicating a robust analgesic effect. Importantly, both respiration and heart rate remained unchanged following exposure to the nociceptive stimulus, and were comparable to values observed under other anesthetics and during natural sleep. CONCLUSIONS: Together with previous studies, these results demonstrate that i.v. chloral hydrate provides excellent anesthetic depth and analgesic efficacy for surgical manipulations in rats.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".