Is Tightly Controlled Disease Activity Possible with Online Patient-reported Outcomes?
Bibliographic record
Abstract
OBJECTIVE: To evaluate the performance of patient-reported outcomes (PRO) as primary indices for identification and prediction of a 28-joint Disease Activity Score (DAS28)>3.2 among patients with rheumatoid arthritis (RA). METHODS: Patients with RA completed monthly online PRO [Health Assessment Questionnaire (HAQ), Rheumatoid Arthritis Disease Activity Index (RADAI), visual analog scale (VAS) fatigue] and were clinically assessed every 3 months using the DAS28. Simple descriptive statistics, logistic regression, and the Bayesian joint modeling approach were used to analyze the data. The Bayesian joint model combines the scores and changes in the scores of 3 PRO to predict a DAS28>3.2 at the subsequent timepoint. RESULTS: A group of 159 patients with RA participated. Stratified summaries of the PRO by DAS28 categories at baseline provided incremental values of the PRO for more active disease. However, on an individual level, the DAS28 and the PRO fluctuated over time. The prediction of subsequent DAS score by a single instrument at single timepoints resulted in moderate sensitivity and specificity. Using the intercept and slope of the combined PRO of the first 3 measurements to predict the DAS28 state at 3 months resulted in a sensitivity of 0.81 and a specificity of 0.92. After 10-fold cross validation, the model had a sensitivity of 0.61 and specificity of 0.75 to identify patients with a DAS28>3.2. CONCLUSION: PRO showed fluctuating levels of disease activity over time, while on a group level disease activity stayed the same. Using the changes in RADAI, HAQ, and VAS fatigue over time to predict future DAS28>3.2 resulted in moderate performance after the internal cross-validation of the model (sensitivity 0.61, specificity 0.75).
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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 | 0.051 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".