Matched cohort study of therapeutic strategies to prevent preschool wheezing/asthma attacks
Bibliographic record
Abstract
Background: An inhaled corticosteroid (ICS) or leukotriene receptor antagonist (LTRA) may prevent wheezing/asthma attacks in preschoolers with recurrent wheeze when added to short-acting β-agonist (SABA). Objective: The aim of this historical matched cohort study was to assess the effectiveness of these treatments for preventing wheezing/asthma attacks. Methods: Electronic medical records from the Optimum Patient Care Research Database were used to characterize a UK preschool population (1-5 years old) with two or more episodes of wheezing during 1 baseline year before first prescription (index date) of ICS or LTRA, or repeat prescription of SABA. Children initiating ICS or LTRA on the index date were matched 1:4 to those prescribed only SABA for age, sex, year of index prescription, mean baseline SABA dose, baseline attacks, baseline antibiotic prescriptions, and eczema diagnosis. Wheezing/asthma attacks (defined as asthma-related emergency attendance, hospital admission, or acute oral corticosteroid prescription) during 1 outcome year were compared using conditional logistic regression. Results: Matched ICS and SABA cohorts included 990 and 3,960 children, respectively (61% male; mean [SD] age 3.2 [1.3] years), and matched LTRA and SABA cohorts included 259 and 1,036 children, respectively (65% male; mean [SD] age 2.6 [1.2] years). We observed no significant difference between matched cohorts in the odds of a wheezing/asthma attack: ICS vs SABA, OR (95% CI) 1.01 (0.85-1.19) and LTRA vs SABA, OR (95% CI) 1.28 (0.96-1.72). Conclusion: We found no evidence that initiation of ICS or LTRA therapy is associated with fewer attacks during 1 outcome year than SABA alone for a heterogeneous group of preschool children with recurrent wheeze in the real-life clinical setting.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".