Admissions and Emergency Visits by Late Preterm Singletons and Twins in the First 5 Years: A Population-Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: weeks) in the first 5 years. STUDY DESIGN: weeks' gestation registered in a health administrative database in Ontario, Canada, between April 1, 2002 and December 31, 2012. Admissions and emergency visits from initial postnatal discharge to 5 years were compared between late preterm and term infants adjusting for maternal and infant characteristics. RESULTS: A total of 1,316,931 infants (75,364 late preterm infants) were included. Late preterm infants had more frequent admissions than term infants in the first 5 years in both singletons (adjusted incidence rate ratio [95% confidence interval] = 1.46 [1.42-1.49]) and twins (1.21 [1.11-1.31]). The difference in admissions between late preterm and term infants were smaller in twins than singletons and decreased with children's ages. Twins had less frequent admissions than singletons for late preterm infants, but not for term infants. The emergency visits were more frequent in late preterm than term infants in all the periods. CONCLUSION: Admissions and emergency visits were more frequent in late preterm than term infants through the first 5 years. Admissions were less frequent in late preterm twins than singletons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".