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Record W2502641258 · doi:10.1002/jnr.23844

Pain in autoimmune disorders

2016· review· en· W2502641258 on OpenAlex
Katherine A. Mifflin, Bradley J. Kerr

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Neuroscience Research · 2016
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
Fundersnot available
KeywordsMedicineNeurosciencePsychology

Abstract

fetched live from OpenAlex

Most autoimmune diseases are associated with pathological pain development. Autoimmune diseases with pathological pain include complex regional pain syndrome, rheumatoid arthritis, and Guillian-Barré syndrome to name a few. The present Review explores research linking the immune system to the development of pathological pain in autoimmune diseases. Pathological pain has been linked to T-cell activation and the release of cytokines from activated microglia in the dorsal horn of the spinal cord. New research on the role of autoantibodies in autoimmunity has generated insights into potential mechanisms of pain associated with autoimmune disease. Autoantibodies may act through various mechanisms in autoimmune disorders. These include the alteration of neuronal excitability via specific antigens such as the voltage-gated potassium channel complexes or by mediating bone destruction in rheumatoid arthritis. Although more research must be done to understand better the role of autoantibodies in autoimmune disease related pain, this may be a promising area of research for new analgesic therapeutic targets. © 2016 Wiley Periodicals, Inc.

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.

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)0.010
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.209
GPT teacher head0.502
Teacher spread0.293 · 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