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To compare the data, birth weight and length of the full-term infants were adjusted to 36 GA (gestational age) and of premature infants to 30 GA using Prader Percentiles.
As previously reported in a detailed nonparticipation analysis, the perinatal and neonatal data (birth weight, length of gestation, maternal preeclampsia, days at discharge from the neonatal intensive care unit) for the clinical study participants and non-participants were similar, except for the lower rate of cerebral palsy among participants at 15 months of age [11].
However, within the limitations of our data, birth weight and maternal weight gain were independent risk factors in models that included both variables (data not shown).
Missing data: birth weight and gestation and shoulder dystocia n = 0; Apgar = 25; Apgar = 25; newborn resuscitation n = 6; post-partum haemorrhage n = 2; Episiotomy n = 65.
In order to examine the relationship between postnatal biometric data (birth weight, head circumference and crown-heel length) and the CTI, we examined the dependency of those intervals which clearly correlated to development.
To maximise the use of available data, birth weight was included in all models as a three category variable (normal (BW > = 2500 gm), low birth weight (BW <2500 gm) and birth weight not recorded).
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Overall, 0.01 % of the data on birth weight, 0.05 % of the data on parity, 0.03 % of the data on maternal age were missing.
In 1997, cohort data did not differ from birth registry data regarding birth weight, maternal age and type of delivery.
12 The data available on outcomes are limited by missing data on birth weight, sex and particularly gestation.
We carried out a validation study among 637 BWHS participants born in Massachusetts using birth registry data from the Massachusetts Department of Public Health to corroborate self-reported data on birth weight.
Input data sets would need to include individual-level data on birth weight, gestational age, mortality outcome, and ideally, comparable causes of death.
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