Evidence map›Paper›PMID 34020729›Full record

ArticleZhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics2021

[Application of the artificial intelligence-rapid whole-genome sequencing diagnostic system in the neonatal/pediatric intensive care unit].

Fang Luo, Hao-Min Li

Open access · greenAbstract read
In one paragraph

Article in Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 95% of its field
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

2 authors at 1 institution in 1 country.

Fang LuoDepartment of Pediatrics, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China.
Hao-Min Li
First Affiliated Hospital Zhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pediatric patients in the neonatal intensive care unit (NICU) and the pediatric intensive care unit (PICU) have a high incidence rate of genetic diseases, and early rapid etiological diagnosis and targeted interventions can help to reduce mortality or improve prognosis. Whole-genome sequencing covers more comprehensive information including point mutation, copy number, and structural and rearrangement variations in the intron region and has become one of the powerful diagnostic tools for genetic diseases. Sequencing data require highly professional judgment and interpretation and are returned for clinical application after several weeks, which cannot meet the need for the diagnosis and treatment of genetic diseases in children. This article introduces the clinical application of rapid whole-genome sequencing in the NICU/PICU and briefly describes related techniques of artificial intelligence-rapid whole-genome sequencing diagnostic system, a rapid high-throughput automated platform for the diagnosis of genetic diseases. The diagnostic system introduces artificial intelligence into the processing of data after whole-genome sequencing and can solve the problems of long time and professional interpretation required for routine genome sequencing and provide a rapid diagnostic regimen for critically ill children suspected of genetic diseases within 24 hours, and therefore, it holds promise for clinical application.

Indexed as

Artificial IntelligenceIntensive Care Units, NeonatalChildCritical IllnessHumansInfant, NewbornIntensive Care Units, PediatricWhole Genome Sequencing

Identifiers

PMID34020729
PMCPMC8140348
OpenAlexW3162637597

What OpenQuestion holds

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Registered trials

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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.