Evidence map›Paper›PMID 28418028›Full record

Trial reportScientific reports2017

An algorithm to predict advanced proximal colorectal neoplasia in Chinese asymptomatic population.

Jason Liwen Huang, Ping Chen, Xiaoqin Yuan, Yunlin Wu, Harry Haoxiang Wang, Martin Chisang Wong

Abstract readRandomized Controlled TrialValidation Study
In one paragraph

Trial report in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

6 authors.

Jason Liwen HuangJC School of Public Health and Primary Care, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong SAR, China.
Ping ChenRuijin Hospital North, Shanghai Jiaotong University, Shanghai 201801, China.
Xiaoqin YuanRuijin Hospital North, Shanghai Jiaotong University, Shanghai 201801, China.
Yunlin WuRuijin Hospital North, Shanghai Jiaotong University, Shanghai 201801, China.
Harry Haoxiang WangSchool of Public Health, Sun Yat-sen University, Guangzhou 510080, Guangdong, China.
Martin Chisang WongJC School of Public Health and Primary Care, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong SAR, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to develop and validate a new algorithm that incorporates distal colonoscopic findings to predict advanced proximal neoplasia (APN) in a Chinese asymptomatic population. We collected age, gender, and colonoscopic findings from a prospectively performed colonoscopy study between 2013 and 2015 in a large hospital-based endoscopy unit in Shanghai, China. Eligible subjects were allocated to a derivation group (n = 3,889) and validation group (n = 1,944) by random sampling. A new index for APN and its cut-off level were evaluated from the derivation cohort by binary logistic regression. The model performance was tested in the validation cohort using area under the curve (AUC). Age, gender, and distal finding were found to be independent predictors of APN in the derivation cohort (p < 0.001). Subjects were categorized into Average Risk (AR) and High Risk (HR) based on a cut-off score of 2. The AUC of the derivation and validation cohorts were 0.801 (0.754-0.847) and 0.722 (0.649-0.794), respectively. In the validation cohort, those in the HR group had a 3.57 fold higher risk of APN when compared with the AR group (P < 0.001), requiring 18 (95% CI = 12-28) follow-up colonoscopies to detect 1 APN. This new clinical index is useful to stratify APN risk in Chinese population.

Indexed as

AlgorithmsColonoscopyEarly Detection of CancerModels, BiologicalAgedChinaColorectal NeoplasmsFemaleHumansMaleMiddle AgedPredictive Value of Tests

Identifiers

PMID28418028
PMCPMC5394471

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.