Patient perceptions of glucocorticoids in anti-neutrophil cytoplasmic antibody-associated vasculitis
Bibliographic record
Abstract
Granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic granulomatosis with polyangiitis (EGPA) are multisystem diseases of small blood vessels, collectively known as the anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAV). This study explores the patient's perspective on the use of glucocorticoids, which are still a mainstay of treatment in AAV. Patients with AAV from the UK, USA, and Canada were interviewed, using purposive sampling to include a range of disease manifestations and demographics. The project steering committee, including patient partners, designed the interview prompts and cues about AAV, its treatment, and impact on health-related quality of life. Interviews were transcribed and analysed to establish themes grounded in the data. A treatment-related code was used to focus analysis of salient themes related to glucocorticoid therapy. Fifty interviews were conducted. Individual themes related to therapy with glucocorticoids emerged from the data and were analysed. Three overarching themes emerged: (1) Glucocorticoids are effective at the time of diagnosis and during relapse, and withdrawal can potentiate a flare, (2) glucocorticoids are associated with salient emotional, physical, and social effects (depression, anxiety, irritation, weight gain and change in appearance, diabetes mellitus, effect on family and work); and (3) patient perceptions of balancing the risks and benefits of glucocorticoids. Patients identified the positive aspects of treatment with glucocorticoids; they are fast-acting and effective, but, they voiced concerns about adverse effects and the uncertainty of the dose-reduction process. These results may be informative in the development of novel glucocorticoid-sparing regimens.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".