160. VERITAS: VASCULITIS: EFFECTS OF REMISSION MAINTENANCE THERAPIES ON RELAPSE AND SIDE EFFECTS ON PATIENT PREFERENCES: A STUDY PROTOCOL
Bibliographic record
Abstract
Background: ANCA-associated vasculitis (AAV) is a multi-system disease with a chronically relapsing and remitting course. As new strategies to reduce the risk of relapses are developed and tested in randomized controlled trials, defining the minimally important difference (MID) for relapse prevention is gaining importance. With patient partners, we designed a survey instrument called the Vasculitis: Effects of Remission maintenance Therapies on relapse and Side effects on patient preferences (VERITAS) to assess the MID from the perspective of patients with vasculitis. Methods: An on-line survey was developed with patient partners and piloted in a single centre for use with patients with vasculitis through the online Vasculitis Patient-Powered Research Network (VPPRN). The survey instrument assesses patients’ acceptance of therapy under a range of baseline risks of relapse, effect sizes of treatment on risk of relapse, and risks of serious adverse events. The survey was piloted in a Rheumatology clinic in Hamilton and after three successful iterations without any patient-driven changes the survey version was determined to be final. Survey responses will be summarized as a distribution of the proportions of patients that will accept each level of risk reduction for each level of absolute risk reduction and risk of serious adverse events. Participants will be randomized to either an unnamed therapy or to naming the therapy prednisone, a treatment with which most patients are familiar to further determine whether known treatments alter perceptions of acceptability. The effects of naming of therapy as well as the effects of patient demographics on the MID will be assessed with mixed effects models. Conclusion: Here we report the study protocol for VERITAS, an instrument designed to assess the distribution of preferences of patients with vasculitis for therapies that reduce the risk of relapse. The distribution of preferences will be useful in determining patient-important MID and sample size estimates for future clinical trials. Disclosures: This work was supported by the Vasculitis Clinical Research Consortium and the Vasculitis Foundation.
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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 | 0.033 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.082 | 0.020 |
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".