Health Services Use by Late Preterm and Term Infants From Infancy to Adulthood: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: Late-preterm infants born at 34 to 36 weeks' gestation have increased risks of various health problems. Health service utilization (HSU) of late-preterm infants has not been systematically summarized before. OBJECTIVES: To summarize the published literature on short- and long-term HSU by late-preterm infants versus term infants from infancy to adulthood after initial discharge from the hospital. DATA SOURCES: We searched Medline, Embase, the Cumulative Index to Nursing and Allied Health Literature, and PsycINFO. STUDY SELECTION: Cohort and case-control studies that compared HSU (admissions, emergency department visits, etc) between late-preterm infants and term infants were included. DATA EXTRACTION: Data extracted included study design, setting, population, HSU, covariates, and effect estimates. RESULTS: Fifty-two articles were included (50 cohort and 2 case-control studies). Meta-analyses with random effect models that used the inverse-variance method found that late-preterm infants had higher chances of all-cause admissions than term infants during all the time periods. The magnitude of the differences decreased with age from the neonatal period through adolescence, with adjusted odds ratios from 2.34 (95% confidence intervals 1.19-4.61) to 1.09 (1.05-1.13) and adjusted incidence rate ratios from 2.62 (2.52-2.72) to 1.14 (1.11-1.18). Late-preterm infants had higher rates of various cause-specific HSU than term infants for jaundice, infection, respiratory problems, asthma, and neurologic and/or mental health problems during certain periods, including adulthood. LIMITATIONS: Considerable heterogeneity existed and was partially explained by the variations in the adjustment for multiple births and gestational age ranges of the term infants. CONCLUSIONS: Late-preterm infants had higher risks for all-cause admissions as well as for various cause-specific HSU during the neonatal period through adolescence.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.056 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".