Evidence map›Paper›PMID 38528564›Full record

ReviewEuropean journal of medical research2024

Anesthesia decision analysis using a cloud-based big data platform.

Shuiting Zhang, Hui Li, Qiancheng Jing, Weiyun Shen, Wei Luo, Ruping Dai

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in European journal of medical research, 2024. 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.

  1. 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.

Shuiting ZhangDepartment of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
Hui LiDepartment of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
Qiancheng JingDepartment of Otolaryngology Head and Neck Surgery, Hengyang Medical School, The Affiliated Changsha Central Hospital, University of South China, Changsha, 410000, Hunan, China.
Weiyun ShenDepartment of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
Wei LuoDepartment of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
Ruping DaiDepartment of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China. xyeyyrupingdai@csu.edu.cn.

Funding

Health Commission of Hunan Province W20243113Key Fund Project of Hunan Provincial Department of Education 22A0011National Natural Science Foundation of China 82103641 and 82071347Natural Science Foundation of Hunan Province 2022JJ70061
6 · The paper itself

Abstract

Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications.

Indexed as

AnesthesiaAnesthesiologyAnestheticsBig DataCloud ComputingDecision Support TechniquesHumansAnestheticsAnesthesia analysisBig dataCloud-basedDecision-makingPlatformPrecision medicine

Identifiers

PMID38528564
PMCPMC10962079

What Socratic 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 Socratic graph.