Neuropathic Pain: Mechanisms, Sex Differences, and Potential Therapies for a Global Problem
Bibliographic record
Abstract
The study of chronic pain continues to generate ever-increasing numbers of publications, but safe and efficacious treatments for chronic pain remain elusive. Recognition of sex-specific mechanisms underlying chronic pain has resulted in a surge of studies that include both sexes. A predominant focus has been on identifying sex differences, yet many newly identified cellular mechanisms and alterations in gene expression are conserved between the sexes. Here we review sex differences and similarities in cellular and molecular signals that drive the generation and resolution of neuropathic pain. The mix of differences and similarities reflects degeneracy in peripheral and central signaling processes by which neurons, immune cells, and glia codependently drive pain hypersensitivity. Recent findings identifying critical signaling nodes foreshadow the development of rationally designed, broadly applicable analgesic strategies. However, the paucity of effective, safe pain treatments compels targeted therapies as well to increase therapeutic options that help reduce the global burden of suffering.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".