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Record W2072491176 · doi:10.1097/bor.0b013e3280327933

People with arthritis and their families in rehabilitation, care and research

2007· review· en· W2072491176 on OpenAlexaff
Linda Li, Cheryl Koehn, A.J. Lehman

Bibliographic record

VenueCurrent Opinion in Rheumatology · 2007
Typereview
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineArthritisRehabilitationClinical trialPhysical therapyFamily medicinePathology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: People with arthritis play major roles in treatment and research. This review summarises the current knowledge on tools for enhancing shared-decision making in arthritis care; individual and family involvement in rehabilitation; and the consumer's role in arthritis research. RECENT FINDINGS: There are discrepancies in the use of appropriate arthritis treatment. To facilitate evidence-informed treatment choices, a number of decision aids have been developed. A recent systematic review concluded that decision aids could improve the shared-decision making process in a variety of diseases; but only one clinical trial was found on a musculoskeletal condition (back surgery). The evidence on family member participation in arthritis education programs is mixed, partly due to a lack of content specifically targeting family members in some studies. Finally, people with arthritis are playing important roles as collaborators in research. Early experience indicates a mutually beneficial relationship for both the individual and researchers. SUMMARY: This review offers three recommendations: First, further clinical trials are needed to test the effectiveness of decision aids in arthritis management. Second, education programs involving strong social support training for family members may improve client outcomes. Third, we encourage further studies to examine the experiences and challenges of people living with arthritis when participating as research partners.

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 score0.902
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
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.078
GPT teacher head0.424
Teacher spread0.346 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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