Evidence map›Paper›PMID 34306232›Full record

ArticleJournal of smoking cessation2021

Determining Optimal Cutoffs for Exhaled Carbon Monoxide and Salivary Cotinine to Identify Smokers among Korean Americans in a Smoking Cessation Clinical Trial.

Sun S Kim, Seongho Kim, Philimon N Gona

Open access · hybridAbstract read
In one paragraph

Article in Journal of smoking cessation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 29% of its field
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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Trial
  2. Trial
  3. 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

3 authors at 1 institution in 2 countries.

Sun S KimDepartment of Nursing, University of Massachusetts Boston, USA.ORCID https://orcid.org/0000-0002-5865-0853
Seongho KimDepartment of Social Welfare, Korean Bible University, Republic of Korea.ORCID https://orcid.org/0000-0002-5422-3897
Philimon N GonaDepartment of Exercise and Health, University of Massachusetts Boston, USA.ORCID https://orcid.org/0000-0002-4202-5965
University of Massachusetts Boston · US

Funding

Tobacco Dependence Treatments for Asian AmericansK23DA021243 · NIDA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI KIM, SUN SEOG · 2009 to 2013
$827k
NIDA NIH HHS K23 DA021243
6 · The paper itself

Abstract

introductionIt is critical to accurately identify individuals who continue to smoke even after treatment, as this may prompt the use of more intensive and effective treatment strategies to help them attain complete abstinence.

aimsThis study examined optimal cutoffs for exhaled carbon monoxide (CO) and salivary cotinine to identify smokers among Korean Americans in a smoking cessation clinical trial.

methodsCO and cotinine were measured three to four times over 12 months from the quit day. Statistical analysis was conducted using Receiver Operating Characteristic (ROC) curves.

resultsA CO cutoff of 5 parts per million provided robust sensitivity (80.8-98.3%) and perfect specificity (100%), and a salivary cotinine cutoff of level 2 (30-100 ng/ml) provided the best sensitivity (91.2-95.6%) and perfect specificity (100%). Using these cutoffs, the agreement between self-reports and the two biomarkers ranged from 88.6% to 97.7%. The areas under ROC curves (AUCs) of exhaled CO ranged from 0.90 to 0.99, all of which were significant (all

conclusionExhaled CO and salivary cotinine are complementary, and they should be used together to verify smoking abstinence for smokers in a clinical trial.

Identifiers

PMID34306232
PMCPMC8279201
OpenAlexW3132792598

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.