Animal models for studying the etiology and treatment of low back pain
Bibliographic record
Abstract
Chronic low back pain is a major cause of disability and health care costs. Effective treatments are inadequate for many patients. Animal models are essential to further understanding of the pain mechanism and testing potential therapies. Currently, a number of preclinical models have been developed attempting to mimic aspects of clinical conditions that contribute to low back pain (LBP). This review focused on describing these animal models and the main behavioral tests for assessing pain in each model. Animal models of LBP can be divided into the following five categories: Discogenic LBP, radicular back pain, facet joint osteoarthritis back pain, muscle-induced LBP, and spontaneous occurring LBP models. These models are important not only for enhancing our knowledge of how LBP is generated, but also for the development of novel therapeutic regimens to treat LBP in patients. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1305-1312, 2018.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".