Evidence map›Paper›PMID 34557709›Full record

ReviewInnovation (Cambridge (Mass.))2020

AI-Enabled Wearable and Flexible Electronics for Assessing Full Personal Exposures.

Guangcun Shan, Xin Li, Wei Huang

Abstract readReview
In one paragraph

Review in Innovation (Cambridge (Mass.)), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Review
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  10. Atomic Layer Deposition of Metal Oxides and Chalcogenides for High Performance Transistors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022
    Review
  11. Article
  12. Electro-responsive actuators based on graphene.Innovation (Cambridge (Mass.)) · 2021
    Review
  13. Article
  14. Article
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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

3 authors.

Guangcun ShanSchool of Instrumentation Science and Opto-electronics Engineering, Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Beihang University, 100191 Beijing, China.
Xin LiSchool of Instrumentation Science and Opto-electronics Engineering, Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Beihang University, 100191 Beijing, China.
Wei HuangShaanxi Institute of Flexible Electronics, Northwestern Polytechnical University, 710072 Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research on the exposome has been extended to personal exposures, and full assessment of personal exposures is of great significance for personal health monitoring and epidemiological studies. Compared with static measurement instruments, wearable sensors are more suitable for dynamic personal exposures assessment. The development of flexible wearable sensors with the features of being physically comfortable and easy to use can be a promising solution for the measurement of personal exposures. With the support of big data and AI, large-scale personal exposures assessment could foster the transition from population-based to individual-based epidemiological studies and upgrade the intelligence level of medical services.

Identifiers

PMID34557709
PMCPMC8454737

What OpenQuestion holds

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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.