Evidence map›Paper›PMID 42812055›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University

[Development of a machine learning-based model for assessing pulmonary function in elderly patients].

Cuiyan Chen, Wenxian Zhang, Yilin Cai, Mei Ouyang, Ye Liang, Mingbiao Liang, Huiying Liang

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University. 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

7 authors.

Cuiyan ChenSchool of Public Health, Southern Medical University, Guangzhou 510515, China.
Wenxian ZhangSchool of Public Health, Southern Medical University, Guangzhou 510515, China.
Yilin CaiSchool of Public Health, Southern Medical University, Guangzhou 510515, China.
Mei OuyangSchool of Public Health, Southern Medical University, Guangzhou 510515, China.
Ye LiangGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510000, China.
Mingbiao LiangGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510000, China.
Huiying LiangSchool of Public Health, Southern Medical University, Guangzhou 510515, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo develop a machine learning-based model with good generalization ability for pulmonary function assessment in elderly patients with impaired pulmonary function.

methodsA total of 7720 elderly patients undergoing pulmonary function testing (PFT) in Guangdong Provincial People's Hospital between September 1, 2020 and December 31, 2024 were enrolled. After variable screening by univariate analysis and LASSO regression, the patients were randomly assigned in an 8:2 ratio to the training set (

resultsXGBoost showed the best predictive performance in the elderly patients (

conclusionsThe XGBoost-based model developed in this study is simple and reliable with good generalization ability for pulmonary function assessment in elderly patients.

Indexed as

LungMachine LearningRespiratory Function TestsAgedAged, 80 and overBoosting Machine Learning AlgorithmsFemaleForced Expiratory VolumeHumansMalePredictive Learning ModelsVital Capacityelderlyextreme gradient boostingmachine learningmodel evaluationpulmonary function

Identifiers

PMID42812055
PMCPMC13624982

What OpenQuestion holds

Textmetadata
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.