Severe Neonatal Morbidity Across Gestational Age: Monitoring Infants at High Risk of Mortality
Bibliographic record
Abstract
AIM: This study aimed to quantify temporal trends in severe neonatal morbidity (SNM) and examine its association with neonatal mortality, stratified by gestational age. METHODS: This study included all live births in Sweden from 2007 to 2021. SNM types and subtypes were identified based on diagnoses and procedure codes for births ≥ 22 weeks' gestation, including complications within 27 days. Rates were calculated by gestational age, and temporal changes were assessed using rate ratios (RR) and 95% confidence intervals (CI). Adjusted relative risks (aRR) of neonatal death were also estimated. RESULTS: From 2007 to 2021, 47,048 (2.8%) cases of SNM were identified, rising from 2.2% in 2007 to 3.6% in 2021, mainly due to increased resuscitation/mechanical ventilation rates across all gestational ages. Infections rose among infants born at ≥ 37 weeks (0.59% in 2007-2011 to 0.77% in 2017-2021, RR 1.30, 95% CI, 1.24-1.37), but declined in those born at 22-31 weeks. Neurological morbidity, especially seizures, slightly increased in term and moderately preterm infants. Except for infants born at 22-27 weeks, neonatal mortality risks among infants with SNM were higher in infants with greater gestational ages. CONCLUSION: Despite advances in neonatal care, SNM prevalence in Sweden increased from 2007 to 2021 across all gestational ages.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".