Evidence map›Paper›PMID 32419922›Full record

ArticleF1000Research2019

Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study.

Sameera Senanayake, Adrian Barnett, Nicholas Graves, Helen Healy, Keshwar Baboolal, Sanjeewa Kularatna

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in F1000Research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 29% of its field
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

4 citing papers in PubMed, 8 citations in OpenAlex.

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

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Sameera SenanayakeAustralian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia.ORCID 0000-0002-5606-2046
Adrian BarnettAustralian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia.ORCID 0000-0001-6339-0374
Nicholas GravesAustralian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia.ORCID 0000-0002-5559-3267
Helen HealyRoyal Brisbane Hospital for Women, Brisbane, QLD, 4001, Australia.
Keshwar BaboolalRoyal Brisbane Hospital for Women, Brisbane, QLD, 4001, Australia.
Sanjeewa KularatnaAustralian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia.
Queensland University of Technology · AUThe University of Queensland · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Kidney TransplantationMachine LearningAustraliaGraft RejectionGraft SurvivalHumansNew ZealandPrognosisRetrospective StudiesGraft failureKidney transplantMachine learningRisk prediction models

Identifiers

PMID32419922
PMCPMC7199287
OpenAlexW2982513604

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.