Genetic predictors of human chronic pain conditions
Bibliographic record
Abstract
Chronic pain conditions are multifactorial disorders with a high frequency in the population. Their pathophysiology is often unclear, and treatment is inefficient. During the last 20years, genetic linkage analysis and association studies have made considerable strides toward identifying key molecular contributors to the onset and maintenance of chronic pain. Here, we review the genetic variants that have been implicated in chronic pain conditions, divided into the following etiologically-grouped categories: migraine, musculoskeletal pain disorders, neuropathic pain disorders, and visceral pain disorders. In rare familial monogenic pain conditions several strong-effect mutations have been identified. In contrast, the genetic landscape of common chronic pain conditions suggests minor contributions from a large number of single nucleotide polymorphisms representing different functional pathways. A comprehensive survey of up-to-date genetic association results reveals migraine and musculoskeletal pain to be the most investigated chronic pain disorders, in which nearly half of identified genetic variability alters neurotransmission pathways.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".