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Record W3083898164 · doi:10.1016/j.rdc.2020.07.005

Disparities in Rheumatoid Arthritis Care and Health Service Solutions to Equity

2020· review· en· W3083898164 on OpenAlexafffund
Cheryl Barnabé

Bibliographic record

VenueRheumatic Disease Clinics of North America · 2020
Typereview
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsMedicineRheumatoid arthritisEquity (law)Health equityIntensive care medicinePhysical therapyFamily medicineInternal medicinePublic healthNursing

Abstract

fetched live from OpenAlex

Proximal, intermediate, and distal social determinants of health inform the health of populations. Differences in rheumatoid arthritis outcomes between populations reflect inequities in these determinants. However, health service access, medication availability, and high-quality care interactions can be ensured through health system restructuring and innovations in individual-level care provision. This article summarizes disparities in rheumatoid arthritis care that have been recognized and described in the United States and Canada and proposes models of care and treatment approaches that can support better outcomes for population groups at risk for outcome inequities.

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)0.003
metaresearch head score (Gemma)0.008
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: Review
Teacher disagreement score0.015
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.006
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.000

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 head0.080
GPT teacher head0.397
Teacher spread0.316 · 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

Citations20
Published2020
Admission routes2
Has abstractno

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