Proof‐of‐Concept Study of a Web‐Based Methotrexate Decision Aid for Patients With Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To assess the extent to which an online patient decision aid reduced decisional conflict and improved self-management knowledge/skills in patients who were considering methotrexate for rheumatoid arthritis (RA). METHODS: We used a mixed-methods pre-post study design. Eligible participants had a diagnosis of RA, had been prescribed methotrexate but were unsure about starting it, and had access to the internet. Outcome included the Decisional Conflict Scale, the Methotrexate in RA Knowledge Test, and the Effective Consumer Scale. Paired t-tests were used to assess changes before and after the intervention. Randomly selected participants were interviewed at the end of the study about their experiences with the decision aid. RESULTS: Of 30 participants, 23 were women. Mean ± SD age was 54.9 ± 14.9 years and the median disease duration was 1 year (interquartile range 0.3-5.0 years). Mean ± SD decisional conflict changed from 49.50 ± 23.17 preintervention to 21.83 ± 24.12 postintervention (change -27.67 [95% confidence interval -39.89, -15.44]; P < 0.001). Knowledge of methotrexate improved (mean ± SD 30.62 ± 9.26 preintervention and 41.67 ± 6.81 postintervention; P < 0.001), but there was no change in effective consumer attributes (mean ± SD 68.24 ± 12.46 preintervention and 72.94 ± 12.74 postintervention; P = 0.15). Three themes emerged from interviews of 11 participants: seeking confirmation of one's own knowledge of methotrexate, amplifying reluctance when they encountered information contradicting their own experiences, and clarifying thoughts about the next step during the process. CONCLUSION: Patients' decisional conflict and knowledge improved after using the patient decision aid. Interview findings further highlighted the power of patients' prior knowledge and experiences with RA on how they approach the information presented in a decision aid.
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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 | 0.008 | 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) | 0.008 | 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".