Foreign Body Aspiration in Infants and Toddlers: Recent Trends in British Columbia
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to (1) examine recent trends in the demographics and presentation of children with foreign body aspiration at British Columbia's Children's Hospital and (2) develop safety guidelines regarding feeding nuts and other hard, crunchy foods to infants and toddlers. METHODS: The methods used were a retrospective chart review and a review of swallowing mechanics in early childhood. RESULTS: Between July 1997 and July 2001, 51 children under 3 years of age underwent rigid bronchoscopy for suspected foreign body aspiration. Of these patients, 27 (53%) were 18 months of age or younger. Of these 27 infants and toddlers, 24 (89%) had a witnessed choking event and 22 (81%) had an airway foreign body. Nuts, raw carrots, and popcorn kernels accounted for 14 (64%) of the foreign bodies aspirated by these infants and toddlers. Before 2 years of age, children are poorly equipped to grind and swallow hard, crunchy food because they lack second molars and are still adjusting to the descent of the larynx. CONCLUSIONS: Infants and toddlers in British Columbia have been aspirating foreign bodies at an alarmingly high rate. Most cases would have been prevented with better public awareness. Caregivers should be informed that children under 3 years of age should never be fed nuts or other hard, crunchy foods. A public awareness campaign is 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".