ArticleBMJ open2016
Is omission of free text records a possible source of data loss and bias in Clinical Practice Research Datalink studies? A case-control study.
Article in BMJ open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 8 of them syntheses that pooled it.
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Who cites it
68 citing papers in PubMed, 8 syntheses or guidelines pooled it, 97 citations in OpenAlex.
- The Potential of Research Drawing on Clinical Free Text to Bring Benefits to Patients in the United Kingdom: A Systematic Review of the Literature.Frontiers in digital health · 2021Pooled it
- Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.Journal of biomedical semantics · 2020Pooled it
- Cancer diagnostic tools to aid decision-making in primary care: mixed-methods systematic reviews and cost-effectiveness analysis.Health technology assessment (Winchester, England) · 2020Pooled it
- Understanding the role of GPs' gut feelings in diagnosing cancer in primary care: a systematic review and meta-analysis of existing evidence.The British journal of general practice : the journal of the Royal College of General Practitioners · 2020Pooled it
- Case-finding for common mental disorders in primary care using routinely collected data: a systematic review.Social psychiatry and psychiatric epidemiology · 2019Pooled it
- Weight loss as a predictor of cancer in primary care: a systematic review and meta-analysis.The British journal of general practice : the journal of the Royal College of General Practitioners · 2018Pooled it
- Pooled it
- Lung cancer in symptomatic patients presenting in primary care: a systematic review of risk prediction tools.The British journal of general practice : the journal of the Royal College of General Practitioners · 2017Pooled it
- Identification of patients with epilepsy using automated electronic health records phenotyping.Epilepsia · 2023Trial
- Blood test trend versus single threshold abnormality to discriminate cancer from non-cancer in patients with unexpected weight loss: A retrospective cohort study.PLoS medicine · 2026Article
- The Performance of Large Language Models in Extracting Intestinal Symptoms From Electronic Health Records: Retrospective Observational Study.Journal of medical Internet research · 2026Observational
- Prediagnostic prescription patterns in pancreatic cancer: a retrospective primary care cohort study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026Article
- Using large language models to identify prediagnostic clinical features of ovarian cancer from healthcare records: a population-based case-control study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026Article
- Generalizable multilingual medical text anonymization using generative instruction tuning.Communications medicine · 2026Article
- SurvivEHR: a competing risks, time-to-event foundation model for multiple long-term conditions from primary care electronic health records.NPJ digital medicine · 2026Article
- Ethnic differences in healthcare utilisation and diagnosis after first presentation with breathlessness: a retrospective cohort study using UK primary care records.NPJ primary care respiratory medicine · 2026Article
- Prediagnostic primary care consultations and imaging in emergency-diagnosed versus referred patients with lung cancer: a population-based linked data study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026Article
- Decision support tools for pancreatic cancer detection: external validation in Australian primary care - a retrospective cohort study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026Article
- Diagnostic testing patterns for lung cancer-related symptoms in primary care: a retrospective cohort study.BMJ open respiratory research · 2025Article
- Combining Clinician Expertise with Prompt Engineering enhances Small Language Models Reliability for Cancer Entity Recognition in Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
objectivesTo estimate data loss and bias in studies of Clinical Practice Research Datalink (CPRD) data that restrict analyses to Read codes, omitting anything recorded as text.
designMatched case-control study.
settingPatients contributing data to the CPRD.
participants4915 bladder and 3635 pancreatic, cancer cases diagnosed between 1 January 2000 and 31 December 2009, matched on age, sex and general practitioner practice to up to 5 controls (bladder: n=21 718; pancreas: n=16 459). The analysis period was the year before cancer diagnosis. PRIMARY AND SECONDARY OUTCOME MEASURES: Frequency of haematuria, jaundice and abdominal pain, grouped by recording style: Read code or text-only (ie, hidden text). The association between recording style and case-control status (χ(2) test). For each feature, the odds ratio (OR; conditional logistic regression) and positive predictive value (PPV; Bayes' theorem) for cancer, before and after addition of hidden text records.
resultsOf the 20 958 total records of the features, 7951 (38%) were recorded in hidden text. Hidden text recording was more strongly associated with controls than with cases for haematuria (140/336=42% vs 556/3147=18%) in bladder cancer (χ(2) test, p<0.001), and for jaundice (21/31=67% vs 463/1565=30%, p<0.0001) and abdominal pain (323/1126=29% vs 397/1789=22%, p<0.001) in pancreatic cancer. Adding hidden text records corrected PPVs of haematuria for bladder cancer from 4.0% (95% CI 3.5% to 4.6%) to 2.9% (2.6% to 3.2%), and of jaundice for pancreatic cancer from 12.8% (7.3% to 21.6%) to 6.3% (4.5% to 8.7%). Adding hidden text records did not alter the PPV of abdominal pain for bladder (codes: 0.14%, 0.13% to 0.16% vs codes plus hidden text: 0.14%, 0.13% to 0.15%) or pancreatic (0.23%, 0.21% to 0.25% vs 0.21%, 0.20% to 0.22%) cancer.
conclusionsOmission of text records from CPRD studies introduces bias that inflates outcome measures for recognised alarm symptoms. This potentially reinforces clinicians' views of the known importance of these symptoms, marginalising the significance of 'low-risk but not no-risk' symptoms.
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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.