Evidence map›Paper›PMID 42699058›Full record

ArticleESMO real world data and digital oncology2026

QResearch primary care records linked to national hospital and cancer registry data: a validation study.

S Kulkarni, V Perletta, Z Wang, J W Tomlinson, J Hippisley-Cox, G S Collins, A K Clift, D Dodwell, S R Lord

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Article in ESMO real world data and digital oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

S KulkarniDepartment of Oncology, University of Oxford, Oxford, UK.
V PerlettaDepartment of Oncology, University of Oxford, Oxford, UK.
Z WangNuffield Department of Population Health, University of Oxford, Oxford, UK.
J W TomlinsonRadcliffe Department of Medicine, Oxford Centre for Diabetes, Endocrinology and Metabolism, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
J Hippisley-CoxWolfson Institute of Population Health, Queen Mary University of London, London, UK.
G S CollinsDepartment of Applied Health Sciences, School of Health Sciences, College of Medicine and Health, University of Birmingham, Birmingham, UK.
A K CliftDepartment of Surgery and Cancer, Imperial College London, London, UK.
D DodwellNuffield Department of Population Health, University of Oxford, Oxford, UK.
S R LordDepartment of Oncology, University of Oxford, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Population-based linkage of primary care electronic health records to oncology-relevant datasets enables detailed analysis of cancer care and outcomes. QResearch is a large primary care database containing longitudinal health records from a subset of general practices in England, linked to national hospital, cancer registry, treatment and mortality data. However, the extent to which this primary care-hospital-registry linkage accurately reflects the national cancer population and cancer treatment patterns has not been validated. Materials and methods: This validation study included adults aged ≥18 years registered at QResearch-contributing practices with an incident diagnosis of breast, prostate, colorectal, melanoma, kidney, pancreatic or gastro-oesophageal cancer between 2013 and 2020. Demographic characteristics and proportions treated with systemic anticancer therapy (SACT) or radiotherapy (RT) were compared with national statistics. We evaluated, in the absence of the cancer registry, the ascertainment and representativeness of patients with cancer identifiable in other sources. Results: Between 2013 and 2020, the QResearch-linked dataset captured 14.6% ( Conclusion: The QResearch-linked dataset offers a nationally representative and comprehensive platform for population oncology research. Its integration of detailed primary care information, including comorbidities, medications and lifestyle factors, should enable evaluation of treatment predictors and outcomes across diverse populations.

Indexed as

cancer registrydata linkage studyelectronic health recordsprimary carereal-world datasystemic anticancer therapy

Identifiers

PMID42699058
PMCPMC13542614

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