Level of Asthma Controller Therapy Before Admission to the Hospital
Bibliographic record
Abstract
BACKGROUND: In asthma, choice of controller therapy and adherence to treatment can affect the risk of future severe exacerbations leading to hospitalization. OBJECTIVE: Our objective was to characterize treatment dispensation profiles before hospital admission for asthma. METHODS: Using a 1/97th random sample of the national French claims data, patients with asthma aged 6 to 40 years were identified between 2006 and 2014. Patients with subsequent asthma-related hospitalization were selected. On the basis of controller therapy dispensed in the 12 months before admission, treatment profiles were categorized into clusters, using Ward's minimum-variance hierarchical clustering method. RESULTS: Of 17,846 patients with asthma, we identified 275 patients (1.5%) with an asthma-related hospitalization. Three distinct clusters were identified. The first cluster (63.6%) included patients with few dispensations of any controller medication (<1 unit). The second cluster (32.4%) consisted of patients with frequent dispensations of long-acting beta agonists (LABAs)/inhaled corticosteroids (ICS) in fixed-dose combinations. The third cluster (4%) comprised patients receiving free combinations of ICS and LABAs, with more dispensations of LABAs than of ICS. CONCLUSIONS: In France, before an asthma-related hospitalization, more than 60% of patients received little controller therapy and 4% were exposed to higher dispensation of LABAs than of ICS. These results indicate that a large fraction of asthma-related hospitalizations can potentially be prevented with better pharmacotherapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".