Evidence map›Paper›PMID 33042915›Full record

ReviewFrontiers in pediatrics2020

Predicting Apnoeic Events in Preterm Infants.

Kathleen Lim, Haimin Jiang, Andrew P Marshall, Brian Salmon, Timothy J Gale, Peter A Dargaville

Abstract readReview
In one paragraph

Review in Frontiers in pediatrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Paediatric sleep diagnostics in the 21st century: the era of "sleep-omics"?European respiratory review : an official journal of the European Respiratory Society · 2024
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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

6 authors.

Kathleen LimMenzies Institute for Medical Research, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia.
Haimin JiangSchool of Engineering, College of Science, Engineering and Technology, University of Tasmania, Hobart, TAS, Australia.
Andrew P MarshallMenzies Institute for Medical Research, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia.
Brian SalmonSchool of Engineering, College of Science, Engineering and Technology, University of Tasmania, Hobart, TAS, Australia.
Timothy J GaleSchool of Engineering, College of Science, Engineering and Technology, University of Tasmania, Hobart, TAS, Australia.
Peter A DargavilleMenzies Institute for Medical Research, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Apnoea, a pause in respiration, is almost ubiquitous in preterm infants born before completing 30 weeks gestation. Apnoea often begets hypoxemia and/or bradycardia, and has the potential to result in adverse neurodevelopmental consequences. Our current inability to predict apnoeic events in preterm infants requires apnoea to first be detected by monitoring device/s in order to trigger an intervention by bedside (medical or nursing) staff. Such a reactive management approach is laborious, and makes the consequences of apnoeic events inevitable. Recent technological advances and improved signal processing have allowed the possibility of developing prediction models for apnoeic events in preterm infants. However, the development of such models has numerous challenges and is only starting to show potential. This paper identifies requisite components and current gaps in developing prediction models for apnoeic events, and reviews previous studies on predicting apnoeic events in preterm infants.

Indexed as

apnoea of prematuritymachine learningneonatal intensive carepredictionpreterm infants

Identifiers

PMID33042915
PMCPMC7525015

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.