Evidence map›Paper›PMID 41588990›Full record

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

Semi-Supervised Learning to Improve Generalizability of Cancer Associated-Venous Thromboembolism Risk Prediction Models.

Shuai Jin, Chong Wang, Dan Qin, Baosheng Liang, Lichuan Zhang, Weiyin Gao, Xiao Wang, Bo Jiang, Benqiang Rao, Hanping Shi and 2 more

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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

12 authors.

Shuai JinDepartment of Adult Care, School of Nursing, Capital Medical University, Beijing, China.ORCID 0000-0003-2469-0563
Chong WangDepartment of Gastrointestinal Oncology Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
Dan QinDivision of Medical & Surgical Nursing, School of Nursing, Peking University, Beijing, China.
Baosheng LiangDepartment of Biostatistics, School of Public Health, Peking University, Beijing, China.
Lichuan ZhangSchool of Nursing, Hebei University, Baoding, Hebei, China.
Weiyin GaoOperating Room, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Xiao WangDivision of Medical & Surgical Nursing, School of Nursing, Peking University, Beijing, China.
Bo JiangDepartment of Medical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Benqiang RaoDepartment of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
Hanping ShiDepartment of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
Lihui LiuDepartment of Nursing, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
Qian LuDivision of Medical & Surgical Nursing, School of Nursing, Peking University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ObjectiveThe purpose of this study is to develop and validate an improved CA-VTE risk prediction model based on semi-supervised learning (SSL) algorithm.MethodsThis study used a combined retrospective and prospective cohort design. First, data from 2100 cancer patients in a tertiary hospital in Beijing were retrospectively collected, including a "labeled cohort" with CA-VTE outcomes (

Indexed as

NeoplasmsSupervised Machine LearningVenous ThromboembolismAlgorithmsFemaleHumansMaleMiddle AgedPrediction AlgorithmsPredictive Learning ModelsProspective StudiesRetrospective StudiesRisk AssessmentRisk Factorscancerprediction modelrisk factorssemi-supervised learningvenous thromboembolism

Identifiers

PMID41588990
PMCPMC12847654

What OpenQuestion holds

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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.