Advanced Dynamic Weight Bearing as an Observer-independent Measure of Hyperacute Hypersensitivity in Mice
Bibliographic record
Abstract
Background: Standard methods assessing pain in rodents are often observer dependent, potentially resulting in biased outcomes. Advanced dynamic weight bearing (ADWB) offers an observer-independent approach that can provide objective, reliable data in preclinical pain research. Aims: The aim of this study was to characterize the use of ADWB in assessing murine responses to allyl isothiocyanate (AITC)-induced hyperacute hypersensitivity and identify best practices for use of the device. Methods: Male C57BL/6J mice received intraplantar injections of saline or 0.1% AITC solution and were assessed using the ADWB system; simultaneous observer-dependent durations of paw licking and biting were measured. ADWB data were analyzed using the proprietary software from Bioseb and correlated to observer-dependent results, with parameters assessed to optimize data collected. Results: ADWB detected pain-directed changes in weight and surface area distribution in AITC-treated mice, with paw weight and surface area placement correlating to paw licking and biting. Optimization of adjustable threshold parameters allowed for reduced coefficients of variability and increased duration of validated data. Conclusions: The ADWB assay provides an efficient and unbiased measure of chemical-induced hyperacute hypersensitivity in mice. ADWB detection parameters influence amount of validated data and variability, a consideration for data analysis in future studies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".