Trialing precision medicine for type 2 diabetes.

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, 3 citations in OpenAlex.

1. A generalist precision medication framework using temporal causal inference based on treatment-free physiological profiles.Nature communications · 2026
   Article
2. Understanding Type 2 Diabetes Mellitus Risk Parameters through Intermittent Fasting: A Machine Learning Approach.Nutrients · 2023
   Article

Corrections and comments

• Comment on
  Patient stratification for determining optimal second-line and third-line therapy for type 2 diabetes: the TriMaster study.2023
• Comment on
  Patient preference for second- and third-line therapies in type 2 diabetes: a prespecified secondary endpoint of the TriMaster study.2023

Authors and funding

3 authors at 1 institution in 1 country.

Funding

Abstract

PubMed holds no abstract for this paper.

Indexed as