ArticleMedical science monitor : international medical journal of experimental and clinical research2026
Exploration of the Prediction of the Survival Cycle and Influencing Factors of Chinese Patients With Advanced Cancer Based on Multi-Model Analysis.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND Accurate prediction of survival in patients with advanced cancer is essential for optimizing end-of-life care and allocating palliative resources. However, fragmented clinical data and limited prognostic tools make reliable survival estimation challenging in real-world clinical practice. MATERIAL AND METHODS This retrospective study analyzed data from 842 patients with advanced cancer admitted to the palliative care department of a tertiary hospital in China between 2018 and 2020. Multiple clinical and laboratory indicators were collected, including electrolyte levels, hematological parameters, and biochemical markers. Survival prediction models were developed using traditional Cox regression, LASSO-Cox regression, and forward likelihood ratio (Forward-LR) Cox regression, with 80% of the dataset used for training and 20% for validation. Model performance was evaluated using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) values for predicting survival at 1 to 4 weeks. RESULTS The median survival time of the cohort was 9.7 days, with most patients dying within 4 weeks. Age significantly influenced survival risk (HR=1.010). Several clinical indicators were independently associated with mortality, including potassium, chloride, iron, magnesium, platelet count, and inflammatory markers. Elevated potassium and iron levels increased mortality risk, whereas higher chloride and platelet levels showed protective effects. In model comparisons, LASSO-Cox demonstrated better performance in short-term survival prediction (1-2 weeks), while Forward-LR Cox regression showed superior accuracy for longer-term prediction (3-4 weeks). CONCLUSIONS A multi-model prognostic framework based on routine clinical indicators can effectively predict short-term survival in advanced cancer patients. These models may support clinical decision-making in palliative care and improve end-of-life resource allocation pending external validation.
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.