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Record W1990504663 · doi:10.3899/jrheum.080591

Proposed Metrics for the Determination of Rheumatoid Arthritis Outcome and Treatment Success and Failure

2008· article· en· W1990504663 on OpenAlexvenueno aff
Frederick Wolfe, Kaleb Michaud

Bibliographic record

VenueThe Journal of Rheumatology · 2008
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
FundersBristol-Myers Squibb
KeywordsMedicineRheumatoid arthritisOutcome (game theory)ArthritisPhysical therapyIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Objective Patients with rheumatoid arthritis (RA) and their physicians often disagree as to the success of RA treatment or RA outcomes. However, guidelines (such as EULAR criteria for DAS scores) are heavily weighted toward joint counts and laboratory tests, and no guidelines exist for patient reported outcomes. Our aims were (1) to provide a patient-based definition of successful RA outcome or of treatment success and failure; (2) to describe the characteristics of patients meeting this definition; (3) to describe how external states such as disability and comorbidity influence definitions of health outcome; and (4) to derive surrogate-measure cutpoints for the definition. Methods A total of 20,268 patients with RA (5132 without comorbidity) were studied by recursive partitioning and regression methods to determine best dividing points between RA treatment and outcome success and non-success using 0–10 visual analog scales (VAS) for patient global assessment, pain, fatigue, and RA activity, and a Health Assessment Questionnaire (HAQ) scale. Results 14.5% of all patients and 22.9% of those without comorbidity were very satisfied with their health (success). Patient global at a level ≤ 1.25 best separated success from failure. Mean and median scores for those who were very satisfied were HAQ (0–3 scale) 0.36, 0.12; pain (0–10) 1.1, 0.5; global (0–10) 0.9, 0.5; and fatigue (0–10) 1.5, 1.0. VAS scores increased by approximately 0.5 units for each comorbid condition. Conclusion Patient global at a level ≤ 1.25 best separates patients who are very satisfied with their health from those not very satisfied, regardless of the presence of comorbidity. All scores increase with increasing comorbidity, which must be accounted for when assessing individual patients. Values identified here suggest patients require better outcomes than are found in patients who are in Disease Activity Score-28 remission or OMERACT low disease activity states.

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)0.017
metaresearch head score (Gemma)0.095
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.017
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.095
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.029
GPT teacher head0.298
Teacher spread0.268 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations19
Published2008
Admission routes1
Has abstractyes

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