Animal models of chronic pain: Advances and challenges for clinical translation
Bibliographic record
Abstract
Chronic pain is a global problem that has reached epidemic proportions. An estimated 20% of adults suffer from pain, and another 10% are diagnosed with chronic pain each year (Goldberg and McGee, ). Despite the high prevalence of chronic pain (an estimated 1.5 billion people are afflicted worldwide), much remains to be understood about the underlying causes of this condition, and there is an urgent requirement for better pain therapies. The discovery of novel targets and the development of better analgesics rely on an assortment of preclinical animal models; however, there are major challenges to translating discoveries made in animal models to realized pain therapies in humans. This review discusses common animal models used to recapitulate clinical chronic pain conditions (such as neuropathic, inflammatory, and visceral pain) and the methods for assessing the sensory and affective components of pain in animals. We also discuss the advantages and limitations of modeling chronic pain in animals as well as highlighting strategies for improving the predictive validity of preclinical pain studies. © 2016 Wiley Periodicals, Inc.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".