Antibiotic Treatment of Wheezing in Children With Asthma: What Is the Practice?
Bibliographic record
Abstract
OBJECTIVE: Antibiotics are not recommended for the treatment of wheezing in children with asthma, but little is known about their use. This study was undertaken to evaluate trends and determinants of antibiotic use in children with wheezing during the fiscal years 1995 through 2001. METHODS: Using the population-based health care and prescription databases in Manitoba, Canada, this descriptive study examined time trends in antibiotic prescription use for wheezing episodes in a population of children with asthma. The likelihood of receiving an antibiotic prescription according to child and physician characteristics also was determined. Annual population-based rates of antibiotic prescriptions for wheezing episodes were modeled by age and antibiotic class, using general estimating equations. The odds ratio for receiving an antibiotic prescription according to child demographics and physician factors was determined from hierarchical linear modeling. RESULTS: The antibiotic prescription rate for wheezing decreased by 28% from 708 prescriptions per 1000 children with asthma in 1995 to 511 prescriptions in 2001. Fifteen-fold increases in use were observed for broader spectrum macrolides in preschool children. Twenty-three percent of physician visits for wheezing resulted in an immediate antibiotic prescription, but this percentage increased to 64% for antibiotics that were received within 7 days of the episode. General practitioners prescribed antibiotics more often than did pediatricians. Physicians who were not trained in Canada or the United States were 40% more likely to prescribe antibiotics than their counterparts. CONCLUSIONS: Antibiotic use for wheezing in children declined in the 1990s, but the increased use of broader spectrum macrolides has implications for antibiotic resistance. A link between antibiotic prescribing and physician specialty and location of training identifies opportunities for intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".