Bibliographic record
Abstract
Autoimmune diseases frequently occur in females, and a parallel sexually dimorphic suffering is observed in individuals who suffer chronic pain. Though perception and environment influence the chronicity of pain, this review illustrates examples of specific, evolutionarily preserved, physiological parameters that may be responsible and differentially contribute to chronic pain and affect treatment outcomes in females and males. In females, the immune system may be continuously “primed,” potentially due to the presence of two X chromosomes, each bearing a number of genes involved in immune responsiveness. In the event of nerve injury, declining parity rates could be having repercussions via increased rates of chronic pain or less effectiveness to therapies, which may be associated with a heightened immune cell infiltration into damage-associated sites. Additionally, the female hormone estradiol is both neuroprotective and neurodegenerative, with reproductive cycle– and age-dependent outcomes. There is therefore a need to study neuro-immune-endocrine crosstalk in the context of chronic pain. Autoantibodies have been associated to neural antigens with sensory pathway hyperexcitability in patients, and self-antigens need to be identified by damaged nerves remain to be discovered. Specific T cells release pronociceptive cytokines that directly influence neural firing, and T lymphocytes reactivated by specific antigens may elicit neuroprotective effects by secreting factors that support nerve repair. Modulating immune cells could therefore be a mechanism by which nerve recovery is promoted, with sex-specific outcomes. Investigating neuroimmune homeostasis may inform the selection of specific treatment regimens for females or males and hence may improve chronic pain management by recalibrating the influence of the immune system on the nervous system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".