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Record W3100562576 · doi:10.1186/s13293-020-00339-y

Sex differences in neuro(auto)immunity and chronic sciatic nerve pain

2020· review· en· W3100562576 on OpenAlexafffund
Katja Linher‐Melville, Anita Shah, Gurmit Singh

Bibliographic record

VenueBiology of Sex Differences · 2020
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsMedicineChronic painImmune systemSciaticaImmunologyNeuropathic painNeuroscienceAntigenAutoantibodyContext (archaeology)HomeostasisNerve injuryPsychologyInternal medicineBiologyAntibodyAnesthesiaPhysical therapy

Abstract

fetched live from OpenAlex

Chronic pain occurs with greater frequency in women, with a parallel sexually dimorphic trend reported in sufferers of many autoimmune diseases. There is a need to continue examining neuro-immune-endocrine crosstalk in the context of sexual dimorphisms in chronic pain. Several phenomena in particular need to be further explored. In patients, autoantibodies to neural antigens have been associated with sensory pathway hyper-excitability, and the role of self-antigens released by damaged nerves remains to be defined. In addition, specific immune cells release pro-nociceptive cytokines that directly influence neural firing, while T lymphocytes activated by specific antigens secrete factors that either support nerve repair or exacerbate the damage. Modulating specific immune cell populations could therefore be a means to promote nerve recovery, with sex-specific outcomes. Understanding biological sex differences that maintain, or fail to maintain, neuroimmune homeostasis may inform the selection of sex-specific treatment regimens, improving chronic pain management by rebalancing neuroimmune feedback. Given the significance of interactions between nerves and immune cells in the generation and maintenance of neuropathic pain, this review focuses on sex differences and possible links with persistent autoimmune activity using sciatica as an example.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.073
GPT teacher head0.325
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations16
Published2020
Admission routes2
Has abstractyes

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