Antibiotic prescription and food allergy in young children
Bibliographic record
Abstract
BACKGROUND: To assess the relationship between any systemic antibiotic prescription within the first year of life and the presence of an ICD-9-CM diagnosis code for food allergy (FA). METHODS: This was a matched case-control study conducted using South Carolina Medicaid administrative data. FA cases born between 2007 and 2009 were matched to controls without FA on birth month/year, sex, race/ethnicity. Conditional logistic regression was used to model the adjusted odds ratio (aOR) of FA diagnosis. All models were adjusted for presence of asthma, wheeze, or atopic dermatitis. RESULTS: A total of 1504 cases and 5995 controls were identified. Receipt of an antibiotic prescription within the initial 12 months of life was associated with FA diagnosis in unadjusted and adjusted models (aOR 1.21; 95 % CI 1.06-1.39). Compared to children with no antibiotic prescriptions, a linear increase in the aOR was seen with increasing antibiotic prescriptions. Children receiving five or more (aOR 1.64; 95 % CI 1.31-2.05) antibiotic prescriptions were significantly associated with FA diagnosis. The strongest association was noted among recipients of cephalosporin and sulfonamide antibiotics in both unadjusted and adjusted models. CONCLUSIONS: Receipt of antibiotic prescription in the first year of life is associated with FA diagnosis code in young children after controlling for common covariates. Multiple antibiotic prescriptions are more strongly associated with increases in the odds of FA diagnosis.
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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 | 0.001 |
| 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".