An Initiative to Improve Timely Glucocorticoid Tapering in Vasculitis
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: High-dose glucocorticoids (GCs) are required in the initial treatment of systemic vasculitis. However, slow or delayed tapering can lead to unnecessary GC exposure and toxicity. In this quality improvement initiative, we aimed to increase appropriate GC tapering among newly referred patients awaiting specialty consultation at a tertiary vasculitis clinic. METHODS: For each patient referred for anti-neutrophil cytoplasm antibody-associated vasculitis (AAV) or large vessel vasculitis (LVV), recommendation-based GC tapering suggestions were faxed to referring physicians. To maximize uptake, the intervention format was modified according to feedback from referring physicians' offices. The proportion of new patients presenting to their first appointment who (1) had started to taper GCs, (2) were taking their target GC dose according to recommendations, (3) experienced a vasculitis flare during tapering were compared before (July 2017-January 2019) and after (February-October 2019) the intervention. RESULTS: Among 169 consecutive patients referred for AAV or LVV, the proportion who had started to taper GCs by their first visit increased from 84 of 117 (72%) preintervention to 49 of 52 (94%) postintervention (p < 0.01). Mean daily prednisone dose at first visit decreased from 29.9 (SD, 18) mg to 21.7 (SD, 14) mg (p < 0.01). However, the proportion who were ultimately taking "target" GC doses at their first visit did not significantly increase (72% vs. 77%). Disease flares during tapering were similar before and after the intervention (9% vs. 12%). CONCLUSIONS: Patients with AAV and LVV had increased GC tapering and lower GC doses at first visit following a preappointment intervention. Further strategies are needed to improve timely GC tapering in vasculitis.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".