Antibiotic Treatment of Wheezing in Children With Asthma: What Is the Practice?
Bibliographic record
Abstract
Kozyrskyj AL, Dahl ME, Ungar WJ, Becker AB, Law BJ. Pediatrics. 2006;117(6). Available at: www.pediatrics.org/cgi/content/full/117/6/e1104 PURPOSE OF THE STUDY. To evaluate time trends and determinants of antibiotic use in children with wheezing episodes. STUDY POPULATION. Children with asthma were identified from population-based health care and prescription databases in Manitoba, Canada, during fiscal years 1995–2001. Asthma was defined as at least 1 physician or hospital visit for asthma or at least 1 prescription for an asthma drug. METHODS. In this descriptive study, using general estimating equations, annual population-based rates of antibiotic prescriptions for wheezing episodes were modeled by age and antibiotic class. Population-based rates for antibiotic use for wheezing were defined as the annual number of antibiotic prescriptions dispensed per 1000 children with asthma. Linear hierarchical rankings were used to calculate odds ratios for receiving an antibiotic prescription according to child demographics and physician factors. RESULTS. Antibiotic prescription rates for wheezing decreased 28% from 708 prescriptions per 1000 children with asthma in 1995 to 511 prescriptions per 1000 children with asthma in 2001. However, an increase in prescriptions was observed for broader-spectrum macrolides (azithromycin and clarithromycin) in preschool-aged children (a 15-fold increase) and in all children (an eightfold increase). Immediate prescriptions (defined as within 2 days of the visit) were given in 23% of physician encounters for wheezing. Sixty-four percent of the visits resulted in an antibiotic prescription within 7 days of the visit. General practitioners prescribed antibiotics for wheezing more often than pediatricians, as did older compared with younger physicians. Physicians trained outside Canada and the United States were 40% more likely to prescribe antibiotics. Visits for younger children and visits during winter months more frequently resulted in antibiotic prescriptions. CONCLUSIONS. Antibiotic prescription rates for wheezing episodes declined in the late 1990s, but broader-spectrum antibiotic prescription rates increased. REVIEWER COMMENTS. Antibiotic use in asthma has gained renewed interest because of the antiinflammatory properties of certain antibiotic classes such as the macrolides. Coupled with the better-tolerated and more-convenient dosing of newer antibiotics (primarily azithromycin), they may provide a future therapeutic option in the treatment of asthma. Nonetheless, little is published about the prescribing patterns of antibiotics for wheezing and asthma. Coexisting maladies such as otitis or pneumonia are not specifically addressed in this publication but may account for the increase in prescription rates at 7 days postvisit. In addition, the possible contribution of antibiotics in the inception of asthma by participating in the “hygiene hypothesis” provides additional interest in these data. Finally, the perceived dangers of resistance with antibiotic use continue to make their use controversial in the treatment of asthma 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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".