Effectiveness and Risks of Probiotics in Preterm Infants
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness and risks of probiotics among infants born before 34 weeks' gestation and with a birth weight less than 1000 g. METHODS: A population-based retrospective cohort study of infants born before 34 weeks' gestation and admitted to 33 Canadian Neonatal Network (CNN) units between January 1, 2016, and December 31, 2022. We excluded infants who were moribund on admission, died within the first 2 days, were admitted to CNN sites more than 2 days after birth, had major congenital anomalies, or never received enteral feeds. Logistic regression, propensity score-matched, and inverse probability of treatment weighting analyses were applied. RESULTS: Among 32 667 eligible infants born before 34 weeks' gestation, 18 793 (57.5%) (median [IQR] gestational age, 29 [27-31] weeks) received probiotics, and 13 874 (42.5%) (median [IQR] gestational age, 31 [29-33] weeks) did not receive probiotics. In these infants, probiotics were associated with decreased mortality rates (adjusted odds ratio [aOR], 0.62; 98.3% CI, 0.53-0.73) but not decreased rates of necrotizing enterocolitis (NEC) (aOR, 0.92; 98.3% CI, 0.78-1.09) or late-onset sepsis (aOR, 0.90; 98.3% CI, 0.80-1.01). In 7401 infants with a birth weight less than 1000 g, probiotics were associated with decreased mortality rates (aOR, 0.58; 98.3% CI, 0.47-0.71) but not decreased NEC (aOR, 0.90; 98.3% CI. 0.71-1.13) or late-onset sepsis rates (aOR, 1.01; 98.3% CI, 0.86-1.18). Probiotic sepsis occurred in 27 (1.4/1000) infants born before 34 weeks' gestation and 20 (4/1000) infants with a birth weight less than 1000 g. Three infants with probiotic sepsis died, with probiotic sepsis deemed a possible cause in 2 cases. CONCLUSION: Probiotics used in Canadian neonatal units were associated with decreased mortality in infants born before 34 weeks' gestation and with a birth weight less than 1000 g with limited effects on NEC and late-onset sepsis. Probiotic sepsis was rare.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".