Evidence map›Paper›PMID 35873124›Full record

ArticleActa Cardiologica Sinica2022

An Effective Strategy to Activate Physicians to Promote High Cardiovascular Risk Patients to Quit Smoking.

Cheng-Huang Su, Jiann-Shing Jeng, Shih-Te Tu, Chien-Ning Huang, Hung-I Yeh

Open access · greenAbstract read
In one paragraph

Article in Acta Cardiologica Sinica, 2022. 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
0.4field-weighted citation impact, top 42% 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, 3 citations in OpenAlex.

  1. Article
  2. Article
  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

5 authors at 1 institution in 1 country.

Cheng-Huang SuCardiovascular Center, MacKay Memorial Hospital, Taipei & MacKay Medical College, New Taipei City.
Jiann-Shing JengStroke Center and Department of Neurology, National Taiwan University Hospital, Taipei.
Shih-Te TuEndocrinology & Metabolism Division, Changhua Christian Hospital, Changhua.
Chien-Ning HuangInstitute of Medicine, Chung Shan Medical University & Hospital, Taichung, Taiwan.
Hung-I YehCardiovascular Center, MacKay Memorial Hospital, Taipei & MacKay Medical College, New Taipei City.
National Taiwan University Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Adult patients cared for by cardiologists, neurologists, and diabetologists are highly vulnerable to cardiovascular diseases (CVDs), which are worsened by smoking. In the past, physicians of these three specialties at major hospitals in Taiwan always referred patients to family medicine and chest medicine departments for smoking cessation programs. However, the participation rate in these programs was unsatisfactory. Objectives: To encourage cardiologists, neurologists, and diabetologists to provide smoking cessation treatment services (SCTSs) to their patients through an annual contest. Methods: Sequential expert meetings, group training, a contest to reward service quantity and abstinence rate, and an annual awards ceremony were held over the past 3 years. Results: More than 350 cardiologists, neurologists, and diabetologists were certified to provide SCTSs, and in the second half of 2020, 3716 high CVD risk patients entered smoking cessation treatment programs, with an abstinence rate exceeding 30% at 3 months. Conclusions: The strategy used in this study was effective in overcoming physician inertia to provide SCTSs and encourage high CVD risk smokers to quit smoking.

Indexed as

ContestGroup trainingPhysician inertiaQuit smoking

Identifiers

PMID35873124
PMCPMC9295038
OpenAlexW4288045307

What OpenQuestion holds

Textmetadata
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.