ArticleBMC cancer2024
Prognosis and risk factor assessment of patients with advanced lung cancer with low socioeconomic status: model development and validation.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled 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.
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.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Non-Pharmacological Interventions for Cough in Patients With Lung Cancer: A Systematic Review and Meta-Analysis.Journal of clinical nursing · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung cancer, a major global health concern, disproportionately impacts low socioeconomic status (SES) patients, who face suboptimal care and reduced survival. This study aimed to evaluate the prognostic performance of traditional Cox proportional hazards (CoxPH) regression and machine learning models, specifically Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), in patients with advanced lung cancer with low SES.
designA retrospective study.
methodThe 949 patients with advanced lung cancer with low SES who entered the hospice ward of a tertiary hospital in Wuhan, China, from January 2012 to December 2021 were randomized into training and testing groups in a 3:1 ratio. CoxPH regression methods and four machine learning algorithms (DT, RF, SVM, and XGBoost) were used to construct prognostic risk prediction models.
resultsThe CoxPH regression-based nomogram demonstrated reliable predictive accuracy for survival at 60, 90, and 120 days. Among the machine learning models, XGBoost showed the best performance, whereas RF had the lowest accuracy at 60 days, DT at 90 days, and SVM at 120 days. Key predictors across all models included Karnofsky Performance Status (KPS) score, quality of life (QOL) score, and cough symptoms.
conclusionsCoxPH, DT, RF, SVM, and XGBoost models are effective in predicting mortality risk over 60-120 days in patients with advanced lung cancer with low SES. Monitoring KPS, QOL, and cough symptoms is crucial for identifying high-risk patients who may require intensified care. Clinicians should select models tailored to individual patient needs and preferences due to varying prediction accuracies. REPORTING
methodThis study was reported in strict compliance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.