Bibliographic record
Abstract
Pain has two main components: the sensory-discriminative (the quality, intensity and location of pain) and the motivational-affective (the emotional aversiveness of pain reflective of suffering). A plethora of translational preclinical and clinical measures for the sensory-discriminative component exist (e.g., von Frey, cold pressor). However, few existing measures capture the more elusive motivational-affective component, and those that do are hampered as they are not translatable across species. Post-lesion evaluation of facial grimacing of emotion-related areas of the brain suggests that the Mouse Grimace Scale is reflective of the motivational-affective component. Facial expressions of emotion (e.g., fear, anger) are lateralized such that the left side of the face exhibits facial expressions more strongly than the right side. Comparing pain-induced facial grimacing to facial expressions of emotion is one way to determine which component of the pain experience is most captured by the Mouse Grimace Scale. We hypothesized that grimacing would be lateralized to the left side of the face. Examining lateralization of pain-induced facial grimacing is novel to pain research. We examined the asymmetry of pain-induced facial grimacing in CD-1 mice using inflammatory, neuropathic, and reflexive pain. And we found that pain is expressed predominantly on the right side of the face, contrary to other emotions. Our findings have important implications for the measurement of pain, as characterized by suffering, in non-verbal populations and for application in veterinary care settings.
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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) | 0.000 | 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) | 0.004 | 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".