Reproducible and fully automated testing of nocifensive behavior in mice
Bibliographic record
Abstract
ABSTRACT Pain in rodents is often inferred from their withdrawal to noxious stimulation, using the threshold stimulus intensity or response latency to quantify pain sensitivity. This usually involves applying stimuli by hand and measuring responses by eye, which limits reproducibility and throughput to the detriment of preclinical pain research. Here, we describe a device that standardizes and automates pain testing by providing computer-controlled aiming, stimulation, and response measurement. Optogenetic and thermal stimuli are applied to the hind paw using blue and infrared light, respectively. Red light delivered through the same light path assists with aiming, and changes in its reflectance off the paw are used to measure paw withdrawal latency with millisecond precision at a fraction of the cost and data processing associated with high-speed video. Using standard video, aiming was automated by training a neural network to recognize the paws and move the stimulator using motorized linear actuators. Real-time data processing allows for closed-loop control of stimulus initiation and termination. We show that stimuli delivered with this device are significantly less variable than hand-delivered stimuli, and that reducing stimulus variability is crucial for resolving stimulus-dependent variations in withdrawal. Slower stimulus waveforms whose stable delivery is made possible with this device reveal details not evident with typical photostimulus pulses. Moreover, the substage video reveals a wealth of “spontaneous” behaviors occurring before and after stimulation that can considered alongside withdrawal metrics to better assess the pain experience. Automation allows comprehensive testing to be standardized and carried out efficiently.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".