Evidence map›Paper›PMID 33117113›Full record

ArticleTobacco induced diseases2020

Nomogram to predict successful smoking cessation in a Chinese outpatient population.

Ning Zhu, Shanhong Lin, Chao Cao, Ning Xu, Xiaopin Yu, Xueqin Chen

Abstract read
In one paragraph

Article in Tobacco induced diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. 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

6 authors.

Ning ZhuDepartment of Respiratory and Critical Care Medicine, Ningbo First Hospital, Ningbo, China.
Shanhong LinDepartment of Ultrasound, Ningbo First Hospital, Ningbo, China.
Chao CaoDepartment of Respiratory and Critical Care Medicine, Ningbo First Hospital, Ningbo, China.
Ning XuDepartment of Respiratory and Critical Care Medicine, Ningbo First Hospital, Ningbo, China.
Xiaopin YuDepartment of Prevention and Health Care, Ningbo First Hospital, Ningbo, China.
Xueqin ChenDepartment of Traditional Medicine, Ningbo First Hospital, Ningbo, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe study aimed to establish and internally validate a nomogram to predict successful smoking cessation in a Chinese outpatient population.

methodsA total of 278 participants were included, and data were collected from March 2016 to December 2018. Predictors for successful smoking cessation were evaluated by 3-month sustained abstinence rates. Least absolute shrinkage and selection operator (LASSO) regression was used to select variables for the model to predict successful smoking cessation, and multivariable logistic regression analysis was performed to establish a novel predictive model. The discriminatory ability, calibration, and clinical usefulness of the nomogram were determined by the concordance index (C-index), calibration plot, and decision curve analysis, respectively. Internal validation with bootstrapping was performed.

resultsThe nomogram included living with a smoker or experiencing workplace smoking, number of outpatient department visits, reason for quitting tobacco, and varenicline use. The nomogram demonstrated valuable predictive performance, with a C-index of 0.816 and good calibration. A high C-index of 0.804 was reached with interval validation. Decision curve analysis revealed that the nomogram for predicting successful smoking cessation was clinically significant when intervention was conducted at a successful cessation of smoking possibility threshold of 19%.

conclusionsThis novel nomogram for successful smoking cessation can be conveniently used to predict successful cessation of smoking in outpatients.

Indexed as

nomogrampredictorssmokingsmoking cessation

Identifiers

PMID33117113
PMCPMC7586925

What OpenQuestion holds

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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.