Antibiotic Exposure and Dental Health: A Systematic Review
Bibliographic record
Abstract
CONTEXT: The use of antibiotics in young children is widespread and may lead to adverse effects on dental health, including staining, developmental defects, and dental caries. OBJECTIVE: To systematically review the effects of early childhood antibiotic exposure on dental health. DATA SOURCES: Medline (Ovid/PubMed), Embase (Ovid) and Cochrane databases. Study bias was assessed using the Newcastle-Ottawa Scale. STUDY SELECTION: English language articles that reported antibiotic exposure before 8 years of age and 1 or more of the relevant outcomes (dental caries, intrinsic tooth staining, or developmental defects of enamel) were included. DATA EXTRACTION: Data on study population, design, type of antibiotic, outcome measurement, and results were extracted from the identified studies. RESULTS: The initial search yielded 1003 articles of which 34 studies were included. Five of the 18 studies on tetracycline described a dose response relationship between exposure to tetracycline doses of > 20 mg/kg per day and dental staining. Early childhood exposure to doxycycline (at any dose) was not associated with dental staining. There was no clear association between any early childhood antibiotic exposure and dental caries or enamel defects. LIMITATIONS: In all included studies, the main limitations and sources of bias were the lack of comparison groups, inconsistent outcome measures, and lack of adjustment for relevant confounders. CONCLUSIONS: There was no evidence that newer tetracycline formulations (doxycycline and minocycline) at currently recommended dosages led to adverse effects on dental health. Findings regarding antibiotic exposure and developmental defects of enamel or dental caries were inconsistent. Further prospective studies are warranted.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".