Evidence map›Paper›PMID 34129820›Full record

ArticleCancer cell2021

Using real-word data to evaluate the effects of broadening eligibility criteria in oncology trials.

Enrique Sanz-Garcia, Benjamin Haibe-Kains, Lillian L Siu

Abstract readComment
PubMed Publisher
In one paragraph

Article in Cancer cell, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Enrique Sanz-GarciaPrincess Margaret Cancer Centre, University Health Network, Toronto, Canada; Department of Medicine, University of Toronto, Toronto, Canada.
Benjamin Haibe-KainsPrincess Margaret Cancer Centre, University Health Network, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Department of Computer Science, University of Toronto, Toronto, Canada; Ontario Institute for Cancer Research, Toronto, Canada; Vector Institute for Artificial Intelligence, Toronto, Canada; Biostatistics Division, Dalla Lana School of Public Health, Toronto, Canada.
Lillian L SiuPrincess Margaret Cancer Centre, University Health Network, Toronto, Canada; Department of Medicine, University of Toronto, Toronto, Canada. Electronic address: lillian.siu@uhn.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Eligibility criteria restrict patient enrollment in clinical trials. A Nature paper applied a machine-learning algorithm in a real-world database to show that relaxing some criteria may not jeopardize efficacy and safety. This may enable more patients to have earlier access to new therapies and make results more generalizable to clinical practice.

Indexed as

Medical OncologyNeoplasmsAlgorithmsHumansMachine Learning

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.