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Record W4386773447 · doi:10.3899/jrheum.2023-0814

Unequal Treatment: Physical Therapy Utilization in Rheumatoid Arthritis

2023· letter· en· W4386773447 on OpenAlexvenueno aff
Jennifer L. Barton

Bibliographic record

VenueThe Journal of Rheumatology · 2023
Typeletter
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRheumatoid arthritisEthnic groupSocioeconomic statusOddsRheumatologyPhysical therapyOdds ratioInternal medicineHealth equityRace (biology)Sports medicineGerontologyFamily medicinePublic healthLogistic regressionPopulationEnvironmental healthPathology

Abstract

fetched live from OpenAlex

The American College of Rheumatology (ACR) 2022 guidelines for nonpharmacologic management of rheumatoid arthritis (RA) include a conditional recommendation for physical therapy (PT).1 Known disparities by socioeconomic status (SES) and race and ethnicity in RA prevalence, patient-reported outcomes of function, disease activity, and access exist and persist over time.2,3 In this issue of The Journal of Rheumatology , Lane et al present their findings of a cross-sectional study of Medicare data showing disparities in PT utilization by SES and race and ethnicity among persons with RA.4 Improvement in function with exercise and PT has been reported in RA, and a greater number of PT visits is also associated with greater functional improvement5; however, as the authors point out, uptake of PT among persons with RA is low.4 Non-Hispanic Black individuals with self-reported arthritis of any type had lower odds of a rehabilitation visit compared with White individuals,6 but no large study of PT utilization among persons with RA has been reported. A complete understanding of health disparities among persons with RA by SES or race and ethnicity remains elusive, and factors at the patient, clinician, and healthcare system level are yet to be fully understood. One 3-step approach to reducing disparities, outlined by Kilbourne et al, involves first detecting disparities, then understanding their origins, followed by intervening to reduce them.7 Lane et al provide new data on detection and a step forward to better understand disparities in their examination of sociocultural and economic determinants of PT utilization among older, Medicare-insured patients with RA.4 This cross-sectional study examined annual Medicare fee-for-service claims from 2012 to 2016 for PT services among older adults (≥ 65 years) with RA and full coverage (Parts A, B, and D). Race and ethnicity data and … Address correspondence to Dr. J.L. Barton, 3710 SW US Veterans Hospital Road, Portland, OR 97202, USA. Email: bartoje{at}ohsu.edu.

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)0.005
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.032
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0020.002
Scholarly communication0.0030.004
Open science0.0010.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0180.001

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 head0.048
GPT teacher head0.318
Teacher spread0.270 · 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 designObservational
Domainnot available
GenreCommentary

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
Published2023
Admission routes1
Has abstractyes

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