Evidence map›Paper›PMID 38680268›Full record

ArticleWorld journal of clinical cases2024

Machine learning in liver surgery: Benefits and pitfalls.

Rafael Calleja, Manuel Durán, María Dolores Ayllón, Ruben Ciria, Javier Briceño

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of clinical cases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Rafael CallejaHepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain. h12calor@gmail.com.
Manuel DuránHepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain.
María Dolores AyllónHepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain.
Ruben CiriaHepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain.
Javier BriceñoHepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The application of machine learning (ML) algorithms in various fields of hepatology is an issue of interest. However, we must be cautious with the results. In this letter, based on a published ML prediction model for acute kidney injury after liver surgery, we discuss some limitations of ML models and how they may be addressed in the future. Although the future faces significant challenges, it also holds a great potential.

Indexed as

Artificial intelligenceLiver surgeryMachine learningPrediction modelRandom forest

Identifiers

PMID38680268
PMCPMC11045503

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.