Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care.

Daniel Ehrmann, Vinyas Harish, Felipe Morgado, Laura Rosella, Alistair Johnson, Briseida Mema, Mjaye Mazwi

Abstract read

In one paragraph

Article in Frontiers in pediatrics, 2022. 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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Teaching old tools new tricks-preparing emergency medicine for the impact of machine learning-based risk prediction models.CJEM · 2023
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

7 authors.

Daniel EhrmannDepartment of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada.

Vinyas HarishTemerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada.

Felipe MorgadoTemerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada.

Laura RosellaMD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.

Alistair JohnsonMD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.

Briseida MemaDepartment of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada.

Mjaye MazwiDepartment of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pediatric intensivists are bombarded with more patient data than ever before. Integration and interpretation of data from patient monitors and the electronic health record (EHR) can be cognitively expensive in a manner that results in delayed or suboptimal medical decision making and patient harm. Machine learning (ML) can be used to facilitate insights from healthcare data and has been successfully applied to pediatric critical care data with that intent. However, many pediatric critical care medicine (PCCM) trainees and clinicians lack an understanding of foundational ML principles. This presents a major problem for the field. We outline the reasons why in this perspective and provide a roadmap for competency-based ML education for PCCM trainees and other stakeholders.

Indexed as

artificial intelligencelearning curriculamachine learningmedical educationpediatric critical care medicine

Identifiers

PMID35620143

DOI10.3389/fped.2022.864755

PMCPMC9127438

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.