Article in The British journal of nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
5 authors.
Thais Rangel Bousquet CarrilhoDepartment of Obstetrics and Gynaecology, Faculty of Medicine, University of British Columbia, Vancouver, Canada.ORCID 0000-0001-5928-6136
Lisa M BodnarDepartment of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, USA.
Kari JohanssonClinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
Gilberto KacNutritional Epidemiology Observatory, Josué de Castro Nutrition Institute, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.ORCID 0000-0001-8603-9077
Jennifer A HutcheonDepartment of Obstetrics and Gynaecology, Faculty of Medicine, University of British Columbia, Vancouver, Canada.
Funding
UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
UCLA Clinical and Translational Science InstituteUL1TR000124 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DUBINETT, STEVEN M. · 2012 to 2015
$57.0M
Clinical and Translational Science Collaborative of ClevelandUL1TR000439 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI KONSTAN, MICHAEL W. · 2012 to 2016
$50.0M
Indiana Clinical and Translational Sciences InstituteUL1TR001108 · NCATS · INDIANA UNIVERSITY INDIANAPOLIS · PI DENNE, SCOTT C., SHEKHAR, ANANTHA · 2013 to 2017
$23.3M
Preterm Birth in Nulliparous Women: An Understudied Population at Great RiskU10HD063036 · NICHD · RESEARCH TRIANGLE INSTITUTE · PI PARKER, CORETTE BREEDEN · 2010 to 2015
$19.5M
Pregnancy as a Window to Future Cardiovascular Health: Adverse Pregnancy Outcomes Supplement RequestU10HL119991 · NHLBI · RESEARCH TRIANGLE INSTITUTE · PI PARKER, CORETTE BREEDEN · 2013 to 2018
$12.6M
Institute for Clinical and Translational ScienceUL1TR000153 · NCATS · UNIVERSITY OF CALIFORNIA-IRVINE · PI COOPER, DAN M · 2012 to 2015
$12.1M
Innovative approaches to inform evidence-based pregnancy weight gain guidelinesR01HD094777 · NICHD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BODNAR, LISA M, HUTCHEON, JENNIFER ANNE · 2018 to 2021
$2.1M
Prevention of Preterm Birth in high Risk Nulliparous PatientsU10HD063047 · NICHD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WAPNER, RONALD · 2010 to 2014
$1.9M
Preterm Birth in Nulliparous Women: An Understudied Population at Great RiskU10HD063053 · NICHD · UNIVERSITY OF UTAH · PI SILVER, ROBERT M. · 2010 to 2014
$1.7M
Preterm Birth in Nulliparous Women: An Understudied Population at Great Risk U10HD063020 · NICHD · NORTHWESTERN UNIVERSITY AT CHICAGO · PI GROBMAN, WILLIAM ADAM · 2010 to 2014
$1.6M
Preterm Birth in Nulliparous Women: An Understudied Population at Great RiskU10HD063046 · NICHD · UNIVERSITY OF CALIFORNIA-IRVINE · PI WING, DEBORAH A · 2010 to 2014
Pregnancy weight gain standards are charts describing percentiles of weight gain among participants with no risk factors that could adversely affect weight gain. This detailed information is burdensome to collect. We investigated the extent to which exclusion of various pre-pregnancy, pregnancy and postpartum factors impacted the values of pregnancy weight gain percentiles. We examined pregnancy weight gain (kg) among 3178 participants of the US nuMoM2b-Heart Health Study (HHS). We identified five groups of potential exclusion criteria for pregnancy weight gain standards: socio-economic characteristics (group 1), maternal morbidities (group 2), lifestyle/behaviour factors (group 3), adverse neonatal outcomes (group 4) and longer-term adverse outcomes (group 5). We established the impact of different exclusion criteria by comparing the median, 25th and 75th percentiles of weight gain in the full cohort with the values after applying each of the five exclusion criteria groups. Differences > 0·75 kg were considered meaningful. Excluding participants with group 1, 2, 3 or 4 exclusion criteria had no impact on the 25th, median or 75th percentiles of pregnancy weight gain. Percentiles were only meaningfully different after excluding participants in group 5 (longer-term adverse outcomes), which shifted the upper end of the weight gain distribution to lower values (e.g. 75th percentile decreased from 19·6 kg to 17·8 kg). This shift was due to exclusion of participants with excess postpartum weight retention > 5 kg or > 10 kg. Except for excess postpartum weight retention, most potential exclusion criteria for pregnancy weight gain standards did not meaningfully impact chart percentiles.
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.
The impact of cohort inclusion/exclusion criteria on pregnancy weight gain chart percentiles. · full record | Socratic