Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility.

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

3 citing papers in PubMed.

1. Covariance regression with random forests.BMC bioinformatics · 2023
   Article
2. Machine learning-guided covariate selection for time-to-event models developed from a small sample of real-world patients receiving bevacizumab treatment.CPT: pharmacometrics & systems pharmacology · 2022
   Article
3. Update to improve reproducibility and interpretability: A response to "Machine Learning for Tumor Growth Inhibition".CPT: pharmacometrics & systems pharmacology · 2022
   Article

Corrections and comments

• Commented on by
  Update to improve reproducibility and interpretability: A response to "Machine Learning for Tumor Growth Inhibition".2022
• Comment on
  Application of Machine Learning for Tumor Growth Inhibition - Overall Survival Modeling Platform.2021

Authors and funding

3 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

Indexed as