Status Forecasting Based on the Baseline Information Using Logistic Regression.

Xin Zhao, Xiaokai Nie

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

MTGGF: A Metabolism Type-Aware Graph Generative Model for Molecular Metabolite Prediction.Interdisciplinary sciences, computational life sciences · 2025
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

2 authors.

Xin ZhaoSchool of Mathematics, Southeast University, Nanjing 210096, China.ORCID 0000-0002-3604-1643

Xiaokai NieSchool of Automation, Southeast University, Nanjing 210096, China.

Funding

Guangdong Basic and Applied Basic Research Foundation 2020A1515110129Jiangsu Foundation for Innovative and Entrepreneurial Doctor 1108000245Nanjing Scientific and Technological Innovation Foundation for Selected Returned Overseas Chinese Scholars 1107010306 and 1108000241National Natural Science Foundation of China 62103105 and 12171085Natural Science Foundation of Jiangsu Province BK20200347 and BK20210218the Fundamental Research Funds for the Central Universities 2242020R40073, MCCSE2021B02, and 2242020R10053

6 · The paper itself

Abstract

In the status forecasting problem, classification models such as logistic regression with input variables such as physiological, diagnostic, and treatment variables are typical ways of modeling. However, the parameter value and model performance differ among individuals with different baseline information. To cope with these difficulties, a subgroup analysis is conducted, in which models' ANOVA and rpart are proposed to explore the influence of baseline information on the parameters and model performance. The results show that the logistic regression model achieves satisfactory performance, which is generally higher than 0.95 in AUC and around 0.9 in

Indexed as

baseline informationlogistic regressionstatus forecastingsubgroup analysis

Identifiers

PMID37420501

DOI10.3390/e24101481

PMCPMC9601351

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.