External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges.

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. The rate of acute kidney injury (AKI) alert detection by the attending physicians was associated with the prognosis of patients with AKI.Frontiers in public health · 2022
   Trial
2. Machine Learning-Based Prediction of Substance Use in Adolescents in Three Independent Worldwide Cohorts: Algorithm Development and Validation Study.Journal of medical Internet research · 2025
   Article
3. Advancing spine care through AI and machine learning: overview and applications.EFORT open reviews · 2024
   Review
4. Risk prediction models for head and neck cancer: A rapid review.Laryngoscope investigative otolaryngology · 2022
   Review

Corrections and comments

• Erratum for
  External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges.2016

Authors and funding

0 authors.

Funding

Abstract

PubMed holds no abstract for this paper.