Breastfeeding duration and lower respiratory tract infection risk in early childhood: Findings from the Canadian Healthy Infant Longitudinal Development Study (CHILD)
Bibliographic record
Abstract
Objectives: Lower respiratory tract infections (LRTIs) are a leading cause of severe illness in young children, impacting long-term respiratory health. This study examined breastfeeding as a potential protective factor against clinically significant LRTIs in early childhood. Methods: We analyzed 3,348 infants from the Canadian CHILD birth cohort, followed from birth to age five. Clinically significant LRTIs were defined as respiratory infections requiring unscheduled physician visits, emergency department care, or hospitalization. Kaplan-Meier and adjusted Cox regression models were performed, adjusting for demographics, socioeconomic variables, smoking exposure, and asthma/atopy history. Results: Clinically significant LRTIs occurred in 10.5% of children. Exclusive breastfeeding for ≥6 months significantly reduced LRTI risk (aHR 0.59, 95% CI 0.41-0.92) compared to <3 months. Exclusive breastfeeding for 3 to <6 months showed a trend toward reduced risk (aHR 0.80, 95% CI 0.61-1.04). erj;66/suppl_69/PA584/F1 F1 F1 Partial breastfeeding showed no significant effect. Conclusion: Exclusive breastfeeding for six months reduces clinically significant LRTI risk in early childhood. These findings highlight the potential for exclusive breastfeeding during infancy to offer long-term protection against respiratory illnesses beyond the breastfeeding period itself.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".