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Record W4401277216 · doi:10.1002/acr.25408

Barriers to, Facilitators of, and Interventions to Support Treat‐to‐Target Implementation in Rheumatoid Arthritis: A Systematic Review

2024· review· en· W4401277216 on OpenAlexaff
Laure Gossec, Louis Bessette, Ricardo Machado Xavier, Ennio Giulio Favalli, Andrew J.K. Östör, Maya H Buch

Bibliographic record

VenueArthritis Care & Research · 2024
Typereview
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversité Laval
FundersAbbVie
KeywordsPsychological interventionMedicineCritical appraisalMEDLINESystematic reviewPhysical therapyAlternative medicineNursingPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: Treat-to-target is recommended in the management of rheumatoid arthritis (RA) but its implementation is suboptimal. We aimed to identify interventional strategies targeted at improving treat-to-target implementation in RA by systematically reviewing published evidence on barriers to, facilitators of, and interventions to support treat-to-target implementation. METHODS: Systematic and scoping literature searches in PubMed/MEDLINE, BIOSIS Previews, Derwent Drug File, Embase, EMCare, International Pharmaceutical Abstracts, and SciSearch were conducted to identify barriers/facilitators and interventions relating to treat-to-target implementation in RA. The quality of included studies was assessed using Critical Appraisal Skills Programme (CASP) checklists. Data related to barriers/facilitators and interventions were extracted, grouped, and summarized descriptively, and a narrative synthesis was generated. RESULTS: In total, 146 articles were analyzed, of which 123 (84%) included ≥50% of the items assessed by CASP checklists. Of the 146 studies, 76 evaluated treat-to-target barriers and facilitators, from which 329 relevant statements were identified and regrouped into 18 target areas, including health care professional (HCP) or patient knowledge or perceptions; patient-HCP communication or alignment; and time or resources. Overall, 56 interventions were identified from 70 studies across the 18 target areas; 54% addressed disease activity or patient-reported outcome assessments. Of the 56 interventions identified, 36 improved treat-to-target implementation and/or patient outcomes in RA. CONCLUSION: Despite long-established treat-to-target recommendations, there remain many barriers to its implementation. Interventions to improve treat-to-target should be developed further and assessed, with a particular focus on tailoring them to individual countries, regions, and health care settings.

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)0.025
metaresearch head score (Gemma)0.101
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.025
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.101
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0090.010
Bibliometrics0.0090.010
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.060
GPT teacher head0.458
Teacher spread0.398 · 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 designSystematic review
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

Citations7
Published2024
Admission routes1
Has abstractyes

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