Narrow- and Broad-Spectrum Antibiotic Use among U.S. Children
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it