Evidence map›Paper›PMID 35845269›Full record

ArticleEJHaem2021

Using machine learning to predict rapid decline of kidney function in sickle cell anemia.

Fatma Güntürkün, Daiqing Chen, Oguz Akbilgic, Robert L Davis, Ibrahim Karabayir, Maxwell Strome, Yang Dai, Santosh L Saraf, Kenneth I Ataga

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EJHaem, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
  3. Article
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

9 authors.

Fatma GüntürkünCenter for Biomedical Informatics University of Tennessee Health Science Center Memphis USA.
Daiqing ChenDepartment of Bioengineering University of Illinois at Chicago Chicago USA.
Oguz AkbilgicDepartment of Health Informatics and Data Science Loyola University Chicago Maywood USA.
Robert L DavisCenter for Biomedical Informatics University of Tennessee Health Science Center Memphis USA.
Ibrahim KarabayirDepartment of Health Informatics and Data Science Loyola University Chicago Maywood USA.
Maxwell StromeDepartment of Computer Science University of Michigan Ann Arbor USA.
Yang DaiDepartment of Bioengineering University of Illinois at Chicago Chicago USA.
Santosh L SarafDepartment of Medicine University of Illinois at Chicago Chicago USA.
Kenneth I AtagaCenter for Sickle Cell Disease University of Tennessee Health Science Center Memphis USA.ORCID https://orcid.org/0000-0002-4501-9982

Funding

Pathways of Cell-Free Hemoglobin in Sickle Cell NephropathyR01HL153161 · NHLBI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI SARAF, SANTOSH · 2020 to 2024
$2.0M
NHLBI NIH HHS R01 HL153161
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

chronic kidney diseasemachine learning modelspredictive capacityrapid decline of eGFRsickle cell disease

Identifiers

PMID35845269
PMCPMC9176130

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.