Chronic Visceral Pain: New Peripheral Mechanistic Insights and Resulting Treatments
Bibliographic record
Abstract
Chronic visceral pain is one of the most common reasons for patients with gastrointestinal disorders, such as inflammatory bowel disease or disorders of brain-gut interaction, to seek medical attention. It represents a substantial burden to patients and is associated with anxiety, depression, reductions in quality of life, and impaired social functioning, as well as increased direct and indirect health care costs to society. Unfortunately, the diagnosis and treatment of chronic visceral pain is difficult, in part because our understanding of the underlying pathophysiologic basis is incomplete. In this review, we highlight recent advances in peripheral pain signaling and specific physiologic and pathophysiologic preclinical mechanisms that result in the sensitization of peripheral pain pathways. We focus on preclinical mechanisms that have been translated into treatment approaches and summarize the current evidence base for directing treatment toward these mechanisms of chronic visceral pain derived from clinical trials. The effective management of chronic visceral pain remains of critical importance for the quality of life of suffers. A deeper understanding of peripheral pain mechanisms is necessary and may provide the basis for novel therapeutic interventions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".