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Record W3023241482 · doi:10.1136/rmdopen-2019-001084

Management of Fatigue in Rheumatoid Arthritis

2020· review· en· W3023241482 on OpenAlex

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

VenueRMD Open · 2020
Typereview
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineRheumatoid arthritisPhysical therapyMoodQuality of life (healthcare)Psychological interventionFibromyalgiaVisual analogue scaleArthritisDiseasePhysical medicine and rehabilitationClinical psychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Fatigue in rheumatoid arthritis is highly prevalent. It is correlated only weakly with disease activity but more so with pain, mood, personality features, poor sleep, obesity and comorbidities. Fatigue can be measured by many standardised questionnaires and more easily with a Visual Analogue Scale or numeric rating scale. Most patients with RA have some fatigue, and at least one in six have severe fatigue. Chronic pain and depressed mood are also common in RA patients with significant fatigue. It affects function and quality of life and is worse on average in women. Evidence-based treatment for fatigue includes treatment of underlying disease activity (with on average modest improvement of fatigue), exercise programmes and supervised self-management programmes with cognitive-behavioural therapy, mindfulness and reinforcement (such as reminders). The specific programmes for exercise and behavioural interventions are not standardised. Some medications cause fatigue such as methotrexate. More research is needed to understand fatigue and how to treat this common complex symptom in RA that can be the worst symptom for some patients.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.983
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.082
GPT teacher head0.388
Teacher spread0.306 · 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