Gain in Quality-adjusted Life-years in Patients with Rheumatoid Arthritis During 1 Year of Biological Therapy: A Prospective Study in Clinical Practice
Bibliographic record
Abstract
OBJECTIVE: The quality-adjusted life-year (QALY) is used to measure outcome in rheumatoid arthritis (RA) studies; identification of drivers of a gain in QALY might help predict a treatment response. We investigated how changes in components of the Disease Activity Score-28 joints (DAS28) were associated with the European League Against Rheumatism (EULAR) and European Quality of Life 5 Dimensions (EQ-5D) responses; and what baseline variables predicted the change in QALY following 1 year of biological therapy. METHODS: Data were collected at baseline and after 3, 6, and 12 months of biological therapy in Danish patients with RA and included bDAS28, sociodemographic data, comorbidity, Health Assessment Questionnaire (HAQ), and EQ-5D scored using the Danish algorithm. A cross-tabulation based on EULAR versus EQ-5D responses was performed, and the association of each DAS28 component across the EULAR/EQ-5D response groups was tested. Predictors of a change in QALY were assessed in a multiple regression model including baseline clinical and patient-reported data as explanatory variables. RESULTS: In total, 315 patients entered the study; 77% were women, 78% IgM rheumatoid factor-positive, with mean age 55 (SD 13) years, disease duration 10 (SD 8) years, mean DAS28 4.9 (SD 1.2), HAQ score 1.22 (SD 0.70), and EQ-5D score 0.60 (SD 0.19). Sixty-eight percent of patients gained QALY; the mean gain was 0.14 (SD 0.13). The patient global score was strongly correlated with both EULAR and EQ-5D responses. The gain in QALY increased with increasing patient global score and number of swollen joints, but not with C-reactive protein (CRP). CONCLUSION: The subjective patient global score was the best baseline predictor of gain in QALY following biological therapy, while the objective CRP measure had no predictive value. It seems that no sharp demarcation between objective and subjective measures could be determined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".