Regular use of inhaled corticosteroids and the long term prevention of hospitalisation for asthma
Bibliographic record
Abstract
BACKGROUND: Inhaled corticosteroids are effective at preventing asthma morbidity and mortality. Most studies, however, have focused on short term effects, raising uncertainty about their effectiveness in the long term. METHODS: The Saskatchewan Health databases were used to form two population based cohorts of asthma patients aged 5-44 between 1975 and 1991. The first cohort included all subjects from the start of asthma treatment, while the second included subjects hospitalised for asthma from the date of discharge. Subjects were followed up, starting 1 year after cohort entry and continuing until 1997, 54 years of age, or death. The outcome was the first asthma hospital admission and readmission, respectively, to occur during follow up. A nested case-control design was used by which all cases were matched on calendar time and several markers of asthma severity to all available controls within the cohort. RESULTS: The full cohort included 30 569 asthmatic subjects of which 3894 were admitted to hospital for asthma and 1886 were readmitted. The overall rate of asthma hospitalisation was 42.4 per 1000 asthma patients per year. Regular use of inhaled corticosteroids was associated with reductions of 31% in the rate of hospital admissions for asthma (95% confidence interval (CI) 17 to 43) and 39% in the rate of readmission (95% CI 25 to 50). The rate reduction found during the first 4 years of follow up was sustained over the longer term. Regular use of inhaled corticosteroids can potentially prevent between five hospital admissions and 27 readmissions per 1000 asthma patients per year. CONCLUSION: Regular use of low dose inhaled corticosteroids prevents a large proportion of hospital admissions with asthma, both early and later on in the course of the disease.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".