The Prevalence of Nonadherence in Difficult Asthma
Bibliographic record
Abstract
RATIONALE: With the advent of new and expensive therapies for severe refractory asthma, targeting the appropriate patients is important. An important issue is identifying nonadherence with current therapies. The extent of nonadherence in a population with difficult asthma has not been previously reported. OBJECTIVES: To examine the prevalence of nonadherence to corticosteroid medication in a population with difficult asthma referred to a Specialist Clinic and to examine the relationship of poor adherence to asthma outcome. METHODS: General practitioner prescription refill records for the previous 6 months for inhaled combination therapy and short-acting beta-agonists were compared with initial prescriptions and expressed as a percentage. Blood plasma prednisolone and cortisol assay levels were used to examine the utility of these measures in assessing adherence to oral prednisolone. Patient demographics, hospital admissions, lung function, oral prednisolone courses, and quality of life data were analyzed to indentify the variables associated with reduced medication adherence. MEASUREMENTS AND MAIN RESULTS: A total of 182 patients were assessed. Sixty-three patients (35%) filled 50% or fewer inhaled medication prescriptions; 88% admitted poor adherence with inhaled therapy after initial denial. Twenty-one percent of patients filled more than 100% of presciptions, and 45% of subjects filled between 51 and 100% of prescriptions. Twenty-three of 51 patients (45%) prescribed oral steroids were found to be nonadherent. CONCLUSIONS: A significant proportion of patients with difficult-to-control asthma remained nonadherent to corticosteroid therapy. Objective surrogate and direct measures of adherence should be performed as part of a difficult asthma assessment and are important before prescibing expensive novel biological therapies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".