Back Mechanical Sensitivity Assessment in the Rat for Mechanistic Investigation of Chronic Back Pain
Bibliographic record
Abstract
Low back pain is the leading cause of disability worldwide, with dramatic personal, economic, and social consequences. To develop novel therapeutics, animal models are needed to examine the mechanisms and effectiveness of novel therapies from a translational perspective. Several rodent models of back pain are used in current investigations. Surprisingly, however, no standardized behavioral test was validated to assess mechanical sensitivity in back pain models. This is critical to confirm that animals with presumed back pain present local hypersensitivity to nociceptive stimuli, and to monitor sensitivity during interventions designed to relieve back pain. The objective of this study is to lay down a simple and accessible test to assess mechanical sensitivity in the back of rats. A test cage was fabricated specifically for this method; length x width x height: 50 x 20 x 7 cm, having a stainless-steel mesh on the top. This test cage allows the application of mechanical stimuli to the back. To perform the test, the back of the animal is shaved in the region of interest, and the test area is marked to repeat the test on different days, as needed. The mechanical threshold is determined with Von Frey filaments applied to the paraspinal muscles, utilizing the up-down method described previously. The positive responses include (1) muscle twitching, (2) arching (back extension), (3) rotation of the neck (4) scratching or licking the back, and (5) escaping. This behavioral test (Back Mechanical Sensitivity (BMS) test) is useful for mechanistic research with rodent models of back pain for the development of therapeutic interventions for the prevention and management of back pain.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".