Assessing Susceptibility to Carbon Dioxide Gas in Three Rat Strains Using the Loss of Righting Reflex
Bibliographic record
Abstract
Abstract Overdose of carbon dioxide gas (CO2) is a common euthanasia method for rodents; however, CO2 exposure activates nociceptors in rats at concentrations equal to or greater than 37% and is reported to be painful in humans at concentrations equal to or greater than 32.5%. Exposure of rats to CO2 could cause pain before loss of consciousness. We used 2 standardized loss of righting reflex (LORR) methods to identify CO2 concentrations associated with unconsciousness in Wistar, Long–Evans, and Sprague–Dawley rats (n = 28 animals per strain). A rotating, motorized cylinder was used to test LORR while the rat was being exposed to increasing concentrations of CO2. LORR was defined based on a 15-second observation period. The 2 methods were 1) a 1-Paw assessment (the righting reflex was considered to be present if one or more paws contacted the cylinder after the rat was positioned in dorsal recumbency), and 2) a 4-Paw assessment (the righting reflex was considered to be present if all 4 paws contacted the cylinder after the rat was positioned in dorsal recumbency). Data were analyzed with Probit regression, and dose-response curves were plotted. 1-Paw EC95 values (CO2 concentration at which LORR occurred for 95% of the population) were Wistar, 27.2%; Long–Evans, 29.2%; and Sprague–Dawley, 35.0%. 4-Paw EC95 values were Wistar, 26.2%; Long–Evans, 25.9%, and Sprague–Dawley, 31.1%. Sprague–Dawley EC95 values were significantly higher in both 1- and 4-Paw tests as compared with Wistar and Long–Evans rats. No differences were detected between sexes for any strain. The 1-Paw EC95 was significantly higher than the 4-Paw EC95 only for Sprague-Dawley rats. These results suggest that a low number of individual rats from the strains studied may experience pain during CO2 euthanasia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".