Evidence map›Paper›PMID 35686130›Full record

ArticleFrontiers in immunology2022

Machine Learning Models for the Diagnosis and Prognosis Prediction of High-Grade B-Cell Lymphoma.

Hui Kong, Haojie Zhu, Xiaoyun Zheng, Meichen Jiang, Lushan Chen, Lingqiong Lan, Jinhua Ren, Xiaofeng Luo, Jing Zheng, Zhihong Zheng and 3 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.3field-weighted citation impact, top 20% 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

6 citing papers in PubMed, 9 citations in OpenAlex.

  1. Multi-Modal Deep Learning-Based Model to Predict Burkitt Lymphoma Recurrence.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. 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

13 authors at 3 institutions in 1 country.

Hui KongDepartment of Hematology, Fujian Institute of Hematology, Fujian Provincial Key Laboratory of Hematology, Fujian Medical University Union Hospital, Fuzhou, China.
Haojie ZhuDepartment of Hematology, Fujian Institute of Hematology, Fujian Provincial Key Laboratory of Hematology, Fujian Medical University Union Hospital, Fuzhou, China.
Xiaoyun ZhengDepartment of Hematology, Fujian Institute of Hematology, Fujian Provincial Key Laboratory of Hematology, Fujian Medical University Union Hospital, Fuzhou, China.
Meichen JiangDepartment of Pathology, Fujian Medical University Union Hospital, Fuzhou, China.
Lushan ChenDepartment of Pathology, Fujian Medical University Union Hospital, Fuzhou, China.
Lingqiong LanDepartment of Hematology, The Second Hospital of Longyan, Longyan, China.
Jinhua RenDepartment of Hematology, Fujian Institute of Hematology, Fujian Provincial Key Laboratory of Hematology, Fujian Medical University Union Hospital, Fuzhou, China.
Xiaofeng LuoDepartment of Hematology, Fujian Institute of Hematology, Fujian Provincial Key Laboratory of Hematology, Fujian Medical University Union Hospital, Fuzhou, China.
Jing ZhengDepartment of Hematology, Fujian Institute of Hematology, Fujian Provincial Key Laboratory of Hematology, Fujian Medical University Union Hospital, Fuzhou, China.
Zhihong ZhengDepartment of Hematology, Fujian Institute of Hematology, Fujian Provincial Key Laboratory of Hematology, Fujian Medical University Union Hospital, Fuzhou, China.
Zhizhe ChenDepartment of Hematology, Fujian Institute of Hematology, Fujian Provincial Key Laboratory of Hematology, Fujian Medical University Union Hospital, Fuzhou, China.
Jianda HuDepartment of Hematology, Fujian Institute of Hematology, Fujian Provincial Key Laboratory of Hematology, Fujian Medical University Union Hospital, Fuzhou, China.
Ting YangDepartment of Hematology, Fujian Institute of Hematology, Fujian Provincial Key Laboratory of Hematology, Fujian Medical University Union Hospital, Fuzhou, China.
Union Hospital · CNFujian Medical University · CNLongyan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-grade B-cell lymphoma (HGBL) is a newly introduced category of rare and heterogeneous invasive B-cell lymphoma (BCL), which is diagnosed depending on fluorescence

Indexed as

Lymphoma, B-CellMachine LearningCohort StudiesHumansIn Situ Hybridization, FluorescenceL-Lactate DehydrogenasePrognosisL-Lactate Dehydrogenaseclassification modelsclinical characteristicsdiagnostic predictorhigh-grade B-cell lymphomamachine learning

Identifiers

PMID35686130
PMCPMC9171399
OpenAlexW4281400221

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.