Evidence map›Paper›PMID 27296358›Full record

SynthesisBMC gastroenterology2016

Risk prediction models for colorectal cancer in people with symptoms: a systematic review.

Tom G S Williams, Joaquín Cubiella, Simon J Griffin, Fiona M Walter, Juliet A Usher-Smith

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

Synthesis in BMC gastroenterology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 5 pooled it
3.8field-weighted citation impact, top 5% 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

38 citing papers in PubMed, 5 syntheses or guidelines pooled it, 78 citations in OpenAlex.

  1. Risk prediction models for symptomatic patients with bladder and kidney cancer: a systematic review.The British journal of general practice : the journal of the Royal College of General Practitioners · 2022
    Pooled it
  2. A systematic review of the status and methodological considerations for estimating risk of first ever stroke in the general population.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2021
    Pooled it
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  17. Risk prediction models for head and neck cancer: A rapid review.Laryngoscope investigative otolaryngology · 2022
    Review
  18. The COLO-COHORT (Colorectal Cancer Cohort) study: Protocol for a multi-centre, observational research study and development of a consent-for-contact research platform.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2022
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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

5 authors at 2 institutions in 2 countries.

Tom G S WilliamsSchool of Clinical Medicine, University of Cambridge, Cambridge, UK.
Joaquín CubiellaDepartment of Gastroenterology, Complexo Hospitalario Universitario de Ourense, Instituto de Investigación Biomédica Ourense-Vigo-Pontevedra, Ourense, Spain.
Simon J GriffinThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.
Fiona M WalterThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.
Juliet A Usher-SmithThe Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK. jau20@medschl.cam.ac.uk.
University of Cambridge · GBComplejo Hospitalario de Ourense · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundColorectal cancer (CRC) is the fourth leading cause of cancer-related death in Europe and the United States. Detecting the disease at an early stage improves outcomes. Risk prediction models which combine multiple risk factors and symptoms have the potential to improve timely diagnosis. The aim of this review is to systematically identify and compare the performance of models that predict the risk of primary CRC among symptomatic individuals.

methodsWe searched Medline and EMBASE to identify primary research studies reporting, validating or assessing the impact of models. For inclusion, models needed to assess a combination of risk factors that included symptoms, present data on model performance, and be applicable to the general population. Screening of studies for inclusion and data extraction were completed independently by at least two researchers.

resultsTwelve thousand eight hundred eight papers were identified from the literature search and three through citation searching. 18 papers describing 15 risk models were included. Nine were developed in primary care populations and six in secondary care. Four had good discrimination (AUROC > 0.8) in external validation studies, and sensitivity and specificity ranged from 0.25 and 0.99 to 0.99 and 0.46 depending on the cut-off chosen.

conclusionsModels with good discrimination have been developed in both primary and secondary care populations. Most contain variables that are easily obtainable in a single consultation, but further research is needed to assess clinical utility before they are incorporated into practice.

Indexed as

Models, StatisticalColorectal NeoplasmsHumansRisk AssessmentRisk FactorsSensitivity and SpecificityColorectal cancerModelPredictionRiskSymptoms

Identifiers

PMID27296358
PMCPMC4907012
OpenAlexW2417673889

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.