Unlocking the future: Machine learning sheds light on prognostication for early-stage hepatocellular carcinoma: Editorial on "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma".

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

2 citing papers in PubMed.

1. Correspondence to letter to the editor 2 on "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma".Clinical and molecular hepatology · 2025
   Article
2. Correspondence to editorial on "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma".Clinical and molecular hepatology · 2024
   Article

Corrections and comments

• Comment on
  Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma.2024

Authors and funding

4 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

Indexed as