Narrow- and Broad-Spectrum Antibiotic Use among U.S. Children
Bibliographic record
Abstract
OBJECTIVES: To provide updated estimates of narrow- and broad-spectrum antibiotic use among U.S. children. DATA SOURCES: Linked nationally representative data from the 2004-2010 Medical Expenditure Panel Survey Household Component and the 2000 Decennial Census. STUDY DESIGN: Relationships between individual-, family-, and community-level characteristics and the use of antibiotics overall and in the treatment of respiratory tract infections (RTIs) are examined using multinomial choice models. PRINCIPAL FINDINGS: More than one quarter (27.3 percent) of children used at least one antibiotic each year with 12.8 percent using broad-spectrum and 18.5 percent using narrow-spectrum antibiotics. Among children with use, more than two-thirds (68.6 percent) used antibiotics to treat RTIs. Multivariate models revealed many differences across groups in antibiotic use, overall and in the treatment of RTIs. Differential use was associated with a broad range of factors related to need (e.g., age, health status), resources (e.g., insurance status, parental income, and education), race-ethnicity, and Census region. CONCLUSIONS: Despite encouraging reports regarding the declining use of antibiotics, large differences in use associated with resources, race-ethnicity, and Census regions suggest a need for further improvement in the judicious and appropriate prescribing of antibiotics for U.S. children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".