Does the occurrence of a severe asthma exacerbation change the rate of subsequent events?
Bibliographic record
Abstract
ABSTRACT Background Severe exacerbations requiring hospitalization are an important component of the natural history of asthma and a major source of its burden. Whether the occurrence of a severe exacerbation affects the rate of subsequent events has far-reaching implications in asthma management. Methods Using the centralized administrative health databases of British Columbia, Canada (1997/01/01 –2016/03/31), we created an incidence cohort of patients with at least one severe asthma exacerbation, defined as an episode of hospitalization with asthma as the primary diagnosis. We used an accelerated failure time joint frailty model for the time intervals between severe asthma exacerbations. Analyses were conducted separately for pediatric (< 14 years old) and adult (≥14 years old) patients. Results There were 3,039 patients (mean age at baseline 6.4, 35% female) in the pediatric group and 5,459 patients (mean age at baseline 50.8, 68% female) in the adult group, with 16% and 15%, respectively, experiencing at least one severe asthma exacerbation during follow-up. The first follow-up severe asthma exacerbation was associated with an increase of 79% (95% CI: 15% – 186%) in the rate of the subsequent events for the pediatric group. The corresponding value was 186% (95% CI: 85% – 355%) for the adult group. For both groups, the effects of subsequent severe exacerbations were not statistically significant. Conclusion Our findings suggest that among patients who have experienced their first severe asthma exacerbation, preventing the next event can drastically change the course of the disease and reduce the burden of future exacerbations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".