Use of Unsafe Teething Remedies: A Survey.
Bibliographic record
Abstract
OBJECTIVE: Various teething remedies have been widely marketed to caregivers. Unsafe remedies, including teething necklaces and topical anesthetics, have been implicated in adverse events, such as suffocation injuries and death. However, little is known about the extent of their use. Our primary objective was to assess the prevalence of teething remedy use among caregivers. A secondary objective was to determine whether the use of unsafe teething remedies is related to socioeconomic status (SES) or maternal education. METHODS: Children aged 12-18 months visiting primary care providers for routine checkups were included. Children outside that age group and those with chronic medical conditions were excluded. Caregivers completed a questionnaire about their children's teething symptoms and remedies used to relieve them. Unsafe remedies were defined on the basis of American Academy of Pediatrics and Canadian Paediatric Society recommendations and included topical anesthetics, teething necklaces and liquid-filled teething rings. RESULTS: Of the 130 questionnaires, 123 were complete and included in statistical analysis: 98% of families used teething remedies and 67% were unsafe. Of these families, 27% used amber teething necklaces; 28% used more than 1 unsafe remedy. Apart from topical anesthetic use, no significant correlations were found between overall unsafe remedy use and SES or maternal education. CONCLUSION: Unsafe teething remedies are commonly used despite recommendations against them. Use of unsafe teething remedies transcends SES and education level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".