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Record W1993714304 · doi:10.1002/art.23931

An evidence‐informed, integrated framework for rheumatoid arthritis care

2008· review· en· W1993714304 on OpenAlexaffabout
Linda Li, Elizabeth M. Badley, Crystal MacKay, Dianne Mosher, Shahin Jamal, Anamaria Jones, Claire Bombardier

Bibliographic record

VenueArthritis Care & Research · 2008
Typereview
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsInstitute for Work & HealthMount Sinai HospitalQueen Elizabeth II Health Sciences CentreUniversity Health NetworkUniversity of TorontoSt. Michael's HospitalArthritis Research Centre of CanadaUniversity of British Columbia
Fundersnot available
KeywordsRheumatoid arthritisMedicineIntensive care medicinePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Introduction Providing adequate care for persons with rheumatoid arthritis (RA) is an ongoing challenge. Although the current evidence supports the use of disease-modifying antirheumatic drugs (DMARDs) within the first 3 months of symptoms appearing (1–3), delay in DMARD use and other gaps in care have been reported across communities (4–9). The situation has worsened due to the shortage of specialists (10). The process of seeking medical treatment begins with the person’s recognition of the symptoms and the action of visiting a family physician (FP) (Figure 1, levels A and B). The FP then performs the appropriate investigations, and if RA is suspected, the FP refers the person to a rheumatologist (levels B and C) who then conducts further tests, provides a diagnosis, and prescribes DMARDs and other appropriate medications (level D). Next, the person may be referred to the available community resources and/or rehabilitation programs that enable self-management (levels E1–E4), and will be periodically assessed by a rheumatologist (level F). Successful delivery of these interventions is largely dependent on the availability of local programs and the coordination among the rheumatologist, the FP, and other health professionals. In the case of severe joint damage, the person is referred for an orthopedic consultation and surgery may be considered (levels G1–G4). Moving from one level to the next involves a potential wait period, which may be caused by, for example, delays in patients’ and health professionals’ recognition of RA symptoms, delays in referral to rheumatologists, lack of access to specialist care or community resources, or patients’ own choices. Delays may occur at any of the following periods (Figure 1): Wait 1: the time between a person’s development and awareness of the seriousness of the symptoms and the first visit with an FP; Wait 2: the time between the first visit with an FP and the first visit with a rheumatologist; Wait 3: from the first rheumatology visit to the date the patient starts the appropriate therapy; Wait 4: from a patient starting medication to the date when he or she has access to adequate resources that enable self-management; andWait 5: from the decision date for an orthopedic consultation to the date of the patient’s first visit with a surgeon and, subsequently, the date of surgery. The delay between symptom onset and DMARD prescription for individuals with RA is a problem across countries (Waits 1–3), with a median lag time ranging from 6.5 to 19 months (5–9). A few studies have attempted to estimate the length of Wait 1, but the findings are inconsistent. Two studies, a retrospective cohort from the US (11) and a prospective study from Norway (12), estimated a median delay of 4 weeks for the first FP visit. However, more recent research from the UK estimated 12 weeks (13), with 38% of people waiting more than 3 months before seeing an FP (14). The lag time from FP visit to rheumatologist consultation is believed to be a major source of the delay (Wait 2). In a UK study, 44% waited more than 3 months for a specialist referral (14). Recent research from Canada also found a median lag time of 79 days between the FP visit and the first rheumatologist visit (15). In contrast, the Linda C. Li, PT, PhD: University of British Columbia and Arthritis Research Centre of Canada, Vancouver, British Columbia, Canada; Elizabeth M. Badley, DPhil: Arthritis Community Research and Evaluation Unit, Toronto Western Research Institute and University of Toronto, Toronto, Ontario, Canada; Crystal MacKay, PT, MHSc: Arthritis Community Research and Evaluation Unit, Toronto Western Research Institute, Toronto, Ontario, Canada; Dianne Mosher, MD, FRCP(C): Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia, Canada; Shahin (Walji) Jamal, MD, FRCP(C): St. Michael’s Hospital and University of Toronto, Toronto, Ontario, Canada; Anamaria Jones, PT, PhD(Candidate): Arthritis Research Centre of Canada, Vancouver, British Columbia, Canada, and Federal University of Sao Paulo, Sao Paulo, Brazil; Claire Bombardier, MD, FRCP(C): University Health Network, University of Toronto, Institute for Work & Health, and Mount Sinai Hospital, Toronto, Ontario, Canada. Dr. Mosher has received honoraria (less than $10,000) from serving on the advisory board for Pfizer, and has received speaking fees (less than $10,000 each) from Schering, Amgen, and Wyeth. Address correspondence to Linda C. Li, PT, PhD, Arthritis Research Centre of Canada, 895 West 10th Avenue, Room 324, Vancouver, British Columbia V5Z 1L7, Canada. E-mail: lli@arthritisresearch.ca. Submitted for publication December 27, 2007; accepted in revised form April 22, 2008. Arthritis & Rheumatism (Arthritis Care & Research) Vol. 59, No. 8, August 15, 2008, pp 1171–1183 DOI 10.1002/art.23931 © 2008, American College of Rheumatology

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.124
metaresearch head score (Gemma)0.124
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.124
Threshold uncertainty score0.656

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1240.124
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0110.008
Science and technology studies0.0040.011
Scholarly communication0.0210.012
Open science0.0110.018
Research integrity0.0150.015
Insufficient payload (model declined to judge)0.0150.003

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 head0.114
GPT teacher head0.457
Teacher spread0.343 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations38
Published2008
Admission routes2
Has abstractyes

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