Inter-Rater Variability in Nocifensive Behaviour Testing of Rodents
Bibliographic record
Abstract
Chronic pain is a critical health problem with a need for new effective treatments. Nocifensive behaviour testing is used to evaluate animal responses to noxious or painful stimuli and is important in basic pain research for understanding pain mechanisms and developing new treatments. A major problem with this technique, however, is that there can be large differences in baseline measurements even when the same sex and strain of rodent is used. We posit that these differences may be due to variation between testers. This project explored factors that contribute to potential inter-rater variability observed in nocifensive behaviour. Adult Sprague Dawley rats underwent evoked somatosensory testing using von Frey filaments and the simplified up-down (SUDO) approach with two different testers, while other environmental conditions such as location, testing time, and acclimation periods were carefully controlled for. The paw withdrawal threshold between testers was different with baseline scores ranging from 3 g to 18.5 g for individual rats. These results suggest that most observed variation in behavioural outcomes is due to tester differences relative to any other factors. Tester differences that could contribute to this variation include sex, genetics, or odour. These results highlight the importance of maintaining one tester for a series of experiments. We conclude that inter-rater variability is an important factor that must be considered when conducting von Frey nocifensive behaviour testing.
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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".