Artificial intelligence for the prediction of postoperative complications in the critically ill.

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. miR-454-3p alleviates LPS-induced cardiomyocyte damage by negatively regulating STAT3 in sepsis-induced cardiomyopathy.Internal and emergency medicine · 2026
   Article
2. Development and external validation of an interpretable early prediction model for acute kidney injury using TabPFN and routine admission data: a retrospective cohort study.BMC medical informatics and decision making · 2026
   Article
3. The cyber-physical paradigm for lifetime aortic valve management: a synthesis of robotics, artificial intelligence, and augmented reality.Journal of robotic surgery · 2026
   Review
4. The complex task of modelling artificial intelligence workflows for forecasting postoperative risk.Journal of anesthesia, analgesia and critical care · 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

3 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.