Effects of a Web‐Based Patient Decision Aid on Biologic and Small‐Molecule Agents for Rheumatoid Arthritis: Results From a Proof‐of‐Concept Study
Bibliographic record
Abstract
OBJECTIVE: To assess the extent to which ANSWER-2, an interactive online patient decision aid, reduces patients' decisional conflict and improves their medication-related knowledge and self-management capacity. METHODS: We used a pre-post study design. Eligible participants had a diagnosis of rheumatoid arthritis (RA), had been recommended to start using a biologic agent or small-molecule agent or to switch to a new one, and had internet access. Access to ANSWER-2 was provided immediately after enrollment. Outcome measures included 1) the Decisional Conflict Scale (DCS), 2) the Medication Education Impact Questionnaire (MeiQ), and 3) the Partners in Health Scale (PIHS). A paired t-test was used to assess differences pre- and postintervention. RESULTS: The majority of the 50 participants were women (n = 40), and the mean ± SD age of participants was 49.6 ± 12.2 years. The median disease duration was 5 years (25th, 75th percentile: 2, 10 years). The mean ± SD DCS score was 45.9 ± 25.1 preintervention and 25.1 ± 21.8 postintervention (mean change of -21.2 of 100 [95% confidence interval (95% CI) -28.1, -14.4], P < 0.001). Before using ANSWER-2, 20% of participants had a DCS score of <25, compared to 52% of participants after the intervention. Similar results were observed in the PIHS (mean ± SD 25.3 ± 14.8 preintervention and 20.4 ± 13.0 postintervention; mean change of -3.7 of 88 [95% CI -6.3, -1.0], P = 0.009). Findings from the MeiQ were mixed, with statistically significant differences found only in the self-management subscales. CONCLUSION: Patients' decisional conflict decreased and perceived self-management capacity improved after using ANSWER-2. Future research comparing the effectiveness of ANSWER-2 with that of educational material on biologic agents will provide further insight into its value in RA management.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.000 |
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.
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".