Consistency in Reporting of Loss of Righting Reflex for Assessment of General Anesthesia in Rats and Mice: A Systematic Review
Bibliographic record
Abstract
General anesthesia induces a reversible loss of consciousness (LOC), a state that is characterized by the inability to feel pain. Identifying LOC in animals poses unique challenges, because the method most commonly used in humans, responding to questions, cannot be used in animals. For over a century, loss of righting reflex (LORR) has been used to assess LOC in animals. This is the only animal method that correlates directly with LOC in humans and has become the standard proxy measure used in research. However, the reporting of how LORR is assessed varies extensively. This systematic literature review examined the consistency and completeness of LORR methods used in rats and mice. The terms 'righting reflex,' 'anesthesia,' 'conscious,' 'rats,' 'mice,' and their derivatives were used to search 5 electronic databases. The abstracts of the 985 articles identified were screened for indications that the study assessed LORR in mice or rats. Full texts of selected articles were reviewed for LORR methodological completeness, with reported methods categorized by 1) animal placement method, 2) behavioral presence of righting reflex, 3) duration of LORR testing, 4) behavioral LORR, and 5) animal position for testing LORR. Only 22 papers reported on all 5 methodological categories. Of the 22 papers, 21 used unique LORR methodologies, with descriptions of LORR methods differing in at least one category as compared with all other studies. This variability indicates that even papers that included all 5 categories still had substantial differences in their methodological descriptions. These findings reveal substantial inconsistencies in LORR methodology and reporting in the biomedical literature likely compromising study replicability and data interpretation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".