Microglia, sex, and pain: even more twists and turns ahead?
Bibliographic record
Abstract
ABSTRACT: When IASP began 50 years ago, microglia were not on the radar of pain researchers. Indeed, interest only began to develop in earnest 25 years after IASP was established. Since then, there has been an explosion of information on microglia, particularly in animal models of chronic neuropathic pain. Microglia are regularly part of the conversation about neuropathic pain in animals and humans. The past 25 years has seen many unexpected twists and turns: microglia mediating neuropathic pain, profound mechanistic sex differences, helpful as well as hurtful microglia, and even microglia-independent neuropathic pain. Where will the story go in the next 50 years? Given the rapid growth in imaging of the panoply of cells, signaling molecules, and receptors in the central nervous system, it seems inevitable that the field will have determined whether microglia are intermediaries of chronic pain in humans and whether the mechanistic sex differences found in animal models exist in pain in humans. The principles of precision medicine may be applied to cell types and pathways increasingly identified in preclinical studies. However, application to humans will require advanced molecular diagnostics, not yet developed, for this approach to be successful. Alternatively, to develop safe and effective therapeutics, the field may take advantage of the points of convergence that have been revealed by studies on microglia and sex in pain. Time, and substantial efforts by passionate and talented pain researchers and clinicians, will tell. But what an exciting, and provocative journey it will be, undoubtedly full of novel twists and turns.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 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".