Evidence map›Paper›PMID 28100399›Full record

ArticleeLife2017

Mixed outcomes for computational predictions.

Chi Van Dang

Abstract readComment
In one paragraph

Article in eLife, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. On the low reproducibility of cancer studies.National science review · 2018
    Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Chi Van DangAbramson Cancer Center, Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, United States.

Funding

MYC--TRANSCRIPTION AND INTERACTION WITH P107R01CA051497 · NCI · WISTAR INSTITUTE · PI DANG, CHI V. · 1995 to 2025
$5.3M
NCI NIH HHS R01 CA051497
6 · The paper itself

Abstract

Experimental efforts to validate the output of a computational model that predicts new uses for existing drugs highlights the inherently complex nature of cancer biology.

Indexed as

Computational BiologyComputer SimulationGene Expression Regulation, NeoplasticHumansNeoplasmscancer biologycimetidinedrug retargetinghumanmetasciencereplicationreproducibilityReproducibility Project: Cancer Biology

Identifiers

PMID28100399
PMCPMC5245956

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.