Evidence map›Paper›PMID 41771770›Full record

ArticleClinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis

POLE: Development and Validation of a Pulmonary Embolism Prediction Model in Lung Cancer.

Dongmei Wang, Wei Xiong, Xuan Huang, Fan Zhang, Fengming Xu, Fengfeng Han

Abstract readValidation Study
In one paragraph

Article in Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Dongmei WangDepartment of Respiratory Medicine, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0009-0008-9007-6100
Wei XiongDepartment of Respiratory Medicine, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0003-2126-8535
Xuan HuangDepartment of Respiratory Medicine, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0009-0002-0568-1100
Fan ZhangDepartment of Respiratory Medicine, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0009-0003-2462-2277
Fengming XuDepartment of Respiratory Medicine, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0009-0005-3475-6687
Fengfeng HanDepartment of Respiratory Medicine, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0001-5460-2793

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BackgroundPrediction models for cancer-associated pulmonary embolism (PE) in lung cancer patients are scarce. This study aimed to develop and validate a novel model to accurately predict PE risk in this population.MethodsA retrospective cohort (n = 476) was used to identify PE-related risk factors and construct a predictive nomogram using Cox regression. Validation was performed in a prospective cohort (n = 140). The model's performance was compared with the Khorana score.ResultsThe newly developed nomogram termed

Indexed as

Lung NeoplasmsNomogramsPulmonary EmbolismAgedFemaleHumansMaleMiddle AgedPrediction AlgorithmsProspective StudiesRetrospective StudiesRisk Factorslung cancerprediction modelpulmonary embolismrisk assessmentrisk factor

Identifiers

PMID41771770
PMCPMC12954006

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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.