A Consensus Definition of Cataplexy in Mouse Models of Narcolepsy
Bibliographic record
Abstract
People with narcolepsy often have episodes of cataplexy, brief periods of muscle weakness triggered by strong emotions. Many researchers are now studying mouse models of narcolepsy, but definitions of cataplexy-like behavior in mice differ across labs. To establish a common language, the International Working Group on Rodent Models of Narcolepsy reviewed the literature on cataplexy in people with narcolepsy and in dog and mouse models of narcolepsy and then developed a consensus definition of murine cataplexy. The group concluded that murine cataplexy is an abrupt episode of nuchal atonia lasting at least 10 seconds. In addition, theta activity dominates the EEG during the episode, and video recordings document immobility. To distinguish a cataplexy episode from REM sleep after a brief awakening, at least 40 seconds of wakefulness must precede the episode. Bouts of cataplexy fitting this definition are common in mice with disrupted orexin/hypocretin signaling, but these events almost never occur in wild type mice. It remains unclear whether murine cataplexy is triggered by strong emotions or whether mice remain conscious during the episodes as in people with narcolepsy. This working definition provides helpful insights into murine cataplexy and should allow objective and accurate comparisons of cataplexy in future studies using mouse models of narcolepsy.
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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 | 0.035 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".