Increasing Prevalence of Cerebral Palsy Among Very Preterm Infants: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVES: It is unclear whether declines in neonatal and infant mortality have led to changes in the occurrence of cerebral palsy. We conducted a study to examine and investigate recent temporal changes in the prevalence of cerebral palsy in a population-based cohort of very preterm infants who were 24 to 30 weeks of gestational age. METHODS: A population-based cohort of very preterm infants who were born between January 1, 1993, and December 31, 2002, was evaluated by the Perinatal Follow-up Program of Nova Scotia. Follow-up extended to age 2 years to ascertain the presence or absence of cerebral palsy and for overall survival. Infant survival and cerebral palsy rates were compared by year and also in two 5-year periods, 1993-1997 and 1998-2002. Logistic regression analyses were used to identify factors that potentially were responsible for temporal changes in cerebral palsy rates. RESULTS: A total of 672 liveborn very preterm infants were born to mothers who resided in Nova Scotia between 1993 and 2002. Infant mortality among very preterm infants decreased from 256 per 1000 live births in 1993 to 114 per 1000 live births in 2002, whereas the cerebral palsy rates increased from 44.4 per 1000 live births in 1993 to 100.0 per 1000 live births in 2002. Low gestational age, postnatal dexamethasone use, patent ductus arteriosus, severe hyaline membrane disease, resuscitation in the delivery room, and intraventricular hemorrhage were associated with higher rates of cerebral palsy, whereas antenatal corticosteroid use was associated with a lower rate. CONCLUSION: Cerebral palsy has increased substantially among very preterm infants in association with and possibly as a consequence of large declines in infant mortality.
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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 | 0.001 | 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) | 0.001 | 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".