Evidence map›Paper›PMID 32312762›Full record

ArticleThe British journal of general practice : the journal of the Royal College of General Practitioners2020

Association between use of urgent suspected cancer referral and mortality and stage at diagnosis: a 5-year national cohort study.

Thomas Round, Carolynn Gildea, Mark Ashworth, Henrik Møller

Open access · hybridAbstract read
In one paragraph

Article in The British journal of general practice : the journal of the Royal College of General Practitioners, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 2 pooled it
4.3field-weighted citation impact, top 4% 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

45 citing papers in PubMed, 2 syntheses or guidelines pooled it, 76 citations in OpenAlex.

  1. A Bibliometric Analysis and Visualization of Decision Support Systems for Healthcare Referral Strategies.International journal of environmental research and public health · 2022
    Pooled it
  2. Pooled it
  3. Determinants of Physicians' Referrals for Suspected Cancer Given a Risk-Prediction Algorithm: Linking Signal Detection and Fuzzy Trace Theory.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
    Article
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  7. Patients' perceptions on reasons for self-referring to the emergency department shortly before a cancer diagnosis: a qualitative study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2025
    Article
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  10. Strengthening general practice will help improve longevity and could reduce health inequalities.The British journal of general practice : the journal of the Royal College of General Practitioners · 2025
    Article
  11. Achieving earlier diagnosis of symptomatic lung cancer.The British journal of general practice : the journal of the Royal College of General Practitioners · 2025
    Article
  12. Article
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  15. Article
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  17. Transforming post pandemic cancer services.British journal of cancer · 2024
    Review
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  20. 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

4 authors at 2 institutions in 1 country.

Thomas RoundSchool of Population Health and Environmental Sciences, King's College London, London, and National Cancer Registration and Analysis Service, Public Health England, London.
Carolynn GildeaNational Cancer Registration and Analysis Service, Public Health England, London.
Mark AshworthSchool of Population Health and Environmental Sciences.
Henrik MøllerSchool of Cancer and Pharmaceutical Sciences, King's College London, London.
King's College London · GBPublic Health England · GB

Funding

Department of Health DRF-2016-09-054
6 · The paper itself

Abstract

backgroundThere is considerable variation between GP practices in England in their use of urgent referral pathways for suspected cancer.

aimTo determine the association between practice use of urgent referral and cancer stage at diagnosis and cancer patient mortality, for all cancers and the most common types of cancer (colorectal, lung, breast, and prostate). DESIGN AND

settingNational cohort study of 1.4 million patients diagnosed with cancer in England between 2011 and 2015.

methodThe cohort was stratified according to quintiles of urgent referral metrics. Cox proportional hazards regression was used to quantify risk of death, and logistic regression to calculate odds of late-stage (III/IV) versus early-stage (I/II) cancers in relation to referral quintiles and cancer type.

resultsCancer patients from the highest referring practices had a lower hazard of death (hazard ratio [HR] = 0.96; 95% confidence interval [CI] = 0.95 to 0.97), with similar patterns for individual cancers: colorectal (HR = 0.95; CI = 0.93 to 0.97); lung (HR = 0.95; CI = 0.94 to 0.97); breast (HR = 0.96; CI = 0.93 to 0.99); and prostate (HR = 0.88; CI = 0.85 to 0.91). Similarly, for cancer patients from these practices, there were lower odds of late-stage diagnosis for individual cancer types, except for colorectal cancer.

conclusionHigher practice use of referrals for suspected cancer is associated with lower mortality for the four most common types of cancer. A significant proportion of the observed mortality reduction is likely due to earlier stage at diagnosis, except for colorectal cancer. This adds to evidence supporting the lowering of referral thresholds and consequent increased use of urgent referral for suspected cancer.

Indexed as

General PracticeNeoplasmsCohort StudiesEnglandHumansMaleReferral and Consultationcancerearly diagnosisgeneral practiceprimary carereferral and consultation

Identifiers

PMID32312762
PMCPMC7176359
OpenAlexW3017369101

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.