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Record W4409699307 · doi:10.22215/cujs.v3i2.5123

Inter-Rater Variability in Nocifensive Behaviour Testing of Rodents

2025· article· en· W4409699307 on OpenAlexaff
Emma-Lee Procher, Elaine Marshall, Emma Bondar, Annemarie Dedek, Michael E. Hildebrand

Bibliographic record

VenueCarleton undergraduate journal of science. · 2025
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsDalhousie UniversityCarleton University
Fundersnot available
KeywordsNeurosciencePsychology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.354
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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