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Record W3032362697 · doi:10.5770/cgj.23.393

Outcomes with Biological Disease-Modifying Anti-Rheumatic Drugs (bDMARDs) in Older Patients Treated for Rheumatoid Arthritis

2020· article· en· W3032362697 on OpenAlexaffvenue
Ripa Akter, Walter P. Maksymowych, M Martin, David B. Hogan

Bibliographic record

VenueCanadian Geriatrics Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversity of AlbertaUniversity of CalgaryUniversity of Ottawa
Fundersnot available
KeywordsMedicineEtanerceptRheumatoid arthritisInternal medicineDiscontinuationAdverse effectAntirheumatic drugsPharmacovigilanceProspective cohort studyAntirheumatic Agents

Abstract

fetched live from OpenAlex

Background Biological disease-modifying antirheumatic drugs (bDMARDs) are recommended for rheumatoid arthritis (RA), but older patients reportedly experience more adverse events (AEs) and show variable treatment response. The objective of this study was to evaluate AEs and effectiveness of bDMARDs in a cohort of older patients. Methods AE and treatment effectiveness (based on DAS28 scores) data from a prospective provincial pharmacovigilance program for the years 2006–2009 in patients 55–64, 65–74, and 75+ years of age were compared. An intention to treat analysis with chisquare and unpaired t-testing for significance was performed. Results There were a total of 333 patients (156 were aged 55–64, 125 were 65–74, 52 were 75+). Those 75+ had higher disease activity and worse functional status at baseline. Among those 75+, AEs with bDMARDs were more common and likely to lead to discontinuation of therapy, be graded as severe, and classified as infectious (p < .05). Remission rate among those 75+ was significantly higher than patients 65–74. Etanercept was the most commonly used drug in all age groups. Conclusion Patients 75+ treated with bDMARDs are at a significantly greater risk of AEs, including infectious ones. The higher remission found in the oldest age group warrants further study.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.254
Teacher spread0.233 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Geriatrics JournalSame topicRheumatoid Arthritis Research and TherapiesFrench-language works237,207