193: Seasonal Variation in Necrotizing Enterocolitis in Preterm Neonates <30 Weeks Gestation
Bibliographic record
Abstract
Necrotizing enterocolitis (NEC) is a complex disease with a multifactorial etiology affecting 8–10% of all very low birth weight (VLBW) infants. Variability in the incidence of NEC, even in the same unit has been noted throughout the year; however, seasonal variability in the rate of NEC has not been explored. To assess the seasonality of stage 2/3 NEC among preterm infants <30 weeks gestation. Data from participating NICUs in the Canadian Neonatal Network of preterm infants of <30 weeks gestation and birth weight of <1500 grams admitted between January 2010 to December 2013 were retrospectively reviewed. We excluded infants with major congenital anomalies. Rates of NEC during the warmer six months (May–October) were compared to rates from the cooler six months (November–April) and incidence rate ratio (IRR) with 95% CIs was calculated for all NEC and NEC associated with infection (diagnosed within +2 days of NEC). Of the total 7676 eligible infants, 291 (3.8%) developed NEC during warmer months and 211 (2.8%) developed NEC during the rest of the year. Baseline characteristics are as reported in the Table. NEC associated with infection was lower in warmer months. The results of IRR are also reported in the table. There was an increase in the incidence of NEC over the study period (63.3/1000 patients in 2010 vs. 77.5/1000 patients in 2013). The incidence rate of NEC during the warmer months was higher compared to the rest of the year. The reason for this higher incidence is unclear. Further research to confirm or refute these findings is needed. In addition, higher vigilance may be needed during warmer months.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".