Follow-Up on the Use of Machine Learning in Clinical Quality Assurance: Can We Detect Adverse Event Under-Reporting in Oncology Trials?

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. Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022
   Review
2. Follow-up on the Use of Advanced Analytics for Clinical Quality Assurance: Bootstrap Resampling to Enhance Detection of Adverse Event Under-Reporting.Drug safety · 2021
   Article
3. Leveraging analytics to assure quality during the Covid-19 pandemic - The COVACTA clinical study example.Contemporary clinical trials communications · 2020
   Article
4. Harnessing the Power of Quality Assurance Data: Can We Use Statistical Modeling for Quality Risk Assessment of Clinical Trials?Therapeutic innovation & regulatory science · 2020
   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

6 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

Indexed as