Mortality Prediction Modeling for Patients with Breast Cancer Based on Explainable Machine Learning.

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

4 citing papers in PubMed.

1. Machine learning for predicting mortality in women diagnosed with breast cancer in the State of Mato Grosso, Brazil using linked population-based cancer registry and mortality data.PloS one · 2026
   Article
2. Explainable machine learning to compare the overall survival status between patients receiving mastectomy and breast conserving surgeries.Scientific reports · 2025
   Article
3. Predicting in-hospital mortality in patients with alcoholic cirrhosis complicated by severe acute kidney injury: development and validation of an explainable machine learning model.Frontiers in medicine · 2025
   Article
4. Metabolic and Immune Adaptations in Preterm Neonates at Early Postnatal Period: Integrated Analysis of Key Metabolites and Pathways.Human mutation · 2025
   Article

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

Authors and funding

14 authors.

Funding

Abstract

PubMed holds no abstract for this paper.

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