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Record W4386638276 · doi:10.3389/fimmu.2023.1221125

Modifiable risk factors linked to the development of rheumatoid arthritis: evidence, immunological mechanisms and prevention

2023· review· en· W4386638276 on OpenAlexaff
Jeba Atkia Maisha, Hani El‐Gabalawy, Liam J. O’Neil

Bibliographic record

VenueFrontiers in Immunology · 2023
Typereview
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineRheumatoid arthritisDiseaseImmunologyEtiologyPsychological interventionRisk factorImmune systemIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Rheumatoid Arthritis (RA) is a common autoimmune disease that targets the synovial joints leading to arthritis. Although the etiology of RA remains largely unknown, it is clear that numerous modifiable risk factors confer increased risk to developing RA. Of these risk factors, cigarette smoking, nutrition, obesity, occupational exposures and periodontal disease all incrementally increase RA risk. However, the precise immunological mechanisms by which these risk factors lead to RA are not well understood. Basic and translational studies have provided key insights into the relationship between inflammation, antibody production and the influence in other key cellular events such as T cell polarization in RA risk. Improving our general understanding of the mechanisms which lead to RA will help identify targets for prevention trials, which are underway in at-risk populations. Herein, we review the modifiable risk factors that are linked to RA development and describe immune mechanisms that may be involved. We highlight the few studies that have sought to understand if modification of these risk factors reduces RA risk. Finally, we speculate that modification of risk factors may be an appealing avenue for prevention for some at-risk individuals, specifically those who prefer lifestyle interventions due to safety and economic reasons.

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)0.001
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 score0.996
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
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 head0.062
GPT teacher head0.328
Teacher spread0.266 · 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

Citations47
Published2023
Admission routes1
Has abstractyes

Explore more

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