Impact of adherence to fluticasone propionate/salmeterol combination (FSC) therapy on the outcomes of patients with asthma: A population based study
Bibliographic record
Abstract
Background: Low adherence to medications is considered a major barrier to achieving optimal management of asthma. Objective: To assess the association between adherence to treatment with FSC and health care resource utilization (HCRU) in patients with asthma. Methods: A retrospective, observational cohort study utilizing pharmaceutical and medical claims from the Quebec provincial health insurance administrative databases of patients (age ≥ 12 years) with a diagnosis of asthma (ICD-9 493.xx) and ≥1 prescription for FSC dispensed from 1/1/2001 to 12/31/2010. Adherence to treatment was ascertained as compliance (medication possession ratio ≥ 80%) and persistence (no absence of treatment gap ≥ 30 days). Asthma-related outcomes of interest include: use of oral corticosteroid (OCS), ER visit, hospitalizations, ICU stay, GP and respirologist visits. Multivariate logistic regression analyses (MLRA) were used to adjust baseline characteristics. Results: 19,486 patients treated with FSC: mean age was 63 year. The proportion of compliant and persistent patients was 42.7% and 29.3% respectively. MLRA showed that compliant patients had significant reduction in adjusted Odds Ratio (95% CI) of exacerbations [0.48 (0.44, 0.54)], OCS use [0.46 (0.42, 0.52)], ER visits [0.48 (0.36, 0.64)], hospitalizations [0.49 (0.42, 0.57)], ICU admission [0.62 (0.39, 0.98)], respirologist visit [0.88 (0.79, 0.99)] and GP visit [0.63 (0.56, 0.71)]. Similar results were observed for persistent patients. Conclusion: The results show that high compliance and high persistence with FSC were associated with decrease risk of exacerbations and lower HCRU in asthma patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".