Serum prednisolone levels as a marker of oral corticosteroid adherence in severe asthma
Bibliographic record
Abstract
BACKGROUND: Severe asthma is a complex heterogeneous disease typically requiring advanced therapies. Underlying the treatment of all asthma, however, is the consistent recommendation across international guidelines to ensure that adherence to therapy is adequate. Currently, there is no consensus on an objective marker of adherence. METHODS: We performed a prospective observational study of 17 participants taking oral prednisolone using serum prednisolone levels as a marker of adherence, and sputum eosinophilia as a marker of control of type 2 airway inflammation. Based on these biomarkers, we classified participants into a non-adherent and an adherent cohort, and further stratified by the presence of ongoing sputum eosinophilia. RESULTS: We identified 3 non-adherent participants and 14 who were adherent, based on their serum prednisolone levels. Stratification using sputum eosinophil counts identified one participant as having ongoing sputum eosinophilia in the setting of non-adherence, while six were identified as steroid resistant with ongoing sputum eosinophilia despite adherence to oral prednisolone therapy. CONCLUSION: Serum prednisolone can be used an objective marker of adherence in those patients with severe asthma taking daily oral prednisolone. In combination with sputum eosinophil counts, a steroid resistant cohort can be distinguished from one with ongoing inflammation in the setting of non-adherence. This information can then be used by clinicians to differentiate the optimal next steps for treatment in these specific populations. TRIAL REGISTRATION: Participants were recruited as part of the Markers of Inflammation in the Management of Severe Asthma (MIMOSA) study, trial registration ACTRN12616001015437 , 02 August 2016.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".