Evidence map›Paper›PMID 35402862›Full record

ArticleiScience2022

A data-driven health index for neonatal morbidities.

Davide De Francesco, Yair J Blumenfeld, Ivana Marić, Jonathan A Mayo, Alan L Chang, Ramin Fallahzadeh, Thanaphong Phongpreecha, Alex J Butwick, Maria Xenochristou, Ciaran S Phibbs and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Towards a new taxonomy of preterm birth.Journal of perinatology : official journal of the California Perinatal Association · 2025
    Review
  4. Article
  5. Review
  6. Review
4 · The record

Corrections and comments

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

21 authors.

Davide De FrancescoDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Yair J BlumenfeldDepartment of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Ivana MarićDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Jonathan A MayoDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Alan L ChangDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Ramin FallahzadehDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Thanaphong PhongpreechaDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Alex J ButwickDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Maria XenochristouDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Ciaran S PhibbsDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Neda H BidokiDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Martin BeckerDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Anthony CulosDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Camilo EspinosaDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Qun LiuDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Karl G SylvesterDepartment of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA.
Brice GaudilliereDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Martin S AngstDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
David K StevensonDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Gary M ShawDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Nima AghaeepourDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.

Funding

SPRINT: Signature for Pain Recovery IN TeensR61NS114926 · NINDS · STANFORD UNIVERSITY · PI SIMONS, LAURA E · 2019 to 2019
$5.4M
Dietary Modulation of Neuroinflammation in Age-Related Memory DisordersR01AG058417 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SLOAN, RICHARD P, SMALL, SCOTT A · 2018 to 2022
$4.2M
Machine Learning for Integrative Modeling of the Immune System in Clinical SettingsR35GM138353 · NIGMS · STANFORD UNIVERSITY · PI AGHAEEPOUR, NIMA · 2020 to 2024
$2.2M
NIA NIH HHS R01 AG058417NIGMS NIH HHS R35 GM138353NINDS NIH HHS R61 NS114926
6 · The paper itself

Abstract

Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities.

Indexed as

Biological sciencesCell biologyMolecular biology

Identifiers

PMID35402862
PMCPMC8990172

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.