Evidence map›Paper›PMID 36483921›Full record

ArticleComputational and mathematical methods in medicine2022

Nomogram Prediction Model Analysis of Risk Factors for Conversion to Thoracotomy after Thoracoscopic Resection of Lung Cancer and Prognostic Value of Lung Cancer.

Peng Su, Yonggang Zhu, Huilai Lv, Jian Zhang, Chao Huang, Fan Zhang, Ziqiang Tian

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 36% 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, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Peng SuDepartment of Thoracic Fifth, Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011 Hebei, China.ORCID https://orcid.org/0000-0003-4811-4288
Yonggang ZhuDepartment of Thoracic Fifth, Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011 Hebei, China.ORCID https://orcid.org/0000-0001-7431-9282
Huilai LvDepartment of Thoracic Fifth, Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011 Hebei, China.ORCID https://orcid.org/0000-0002-2795-9751
Jian ZhangDepartment of Thoracic Fifth, Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011 Hebei, China.ORCID https://orcid.org/0000-0002-0804-2147
Chao HuangDepartment of Thoracic Fifth, Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011 Hebei, China.ORCID https://orcid.org/0000-0002-7150-3401
Fan ZhangDepartment of Thoracic Fifth, Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011 Hebei, China.ORCID https://orcid.org/0000-0002-8658-4136
Ziqiang TianDepartment of Thoracic Fifth, Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011 Hebei, China.ORCID https://orcid.org/0000-0002-2955-3333
Hebei Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study was aimed at exploring the risk factors for thoracotomy in patients undergoing thoracoscopic resection of lung cancer and further analyzing the factors affecting the prognosis of patients. Ninety-six patients with non-small-cell lung cancer who underwent thoracoscopic pulmonary resection were recruited as the subjects, and they were enrolled into the thoracoscopic group (

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsTuberculosis, PulmonaryAgedHumansPrognosisRisk Factors

Identifiers

PMID36483921
PMCPMC9726246
OpenAlexW4287577640

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.