Trial reportTrials2022
Maximising recruitment of research participants into a general practice based randomised controlled trial concerning lung diagnosis-staff insights from an embedded qualitative study.
Trial report in Trials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01344005 (A Pilot Clinical Trial Looking at the Effect on Lung Cancer Diagnosis of Giving a CXR to Smokers Aged Over 60 With Chest Symptoms), which is not on this map. Cited by 4 papers.
What it found
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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.
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.
A Pilot Clinical Trial Looking at the Effect on Lung Cancer Diagnosis of Giving a CXR to Smokers Aged Over 60 With Chest Symptoms
Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Comparing in-person and remote consent of people with dementia into a primary care-based cluster randomised controlled trial: lessons from the Dementia PersonAlised Care Team (D-PACT) feasibility study.BMC medical research methodology · 2025Trial
- Cardiorespiratory and Glycaemic Changes Following Prehospital Nebulised Salbutamol in Acute Asthma: A Mixed Retrospective and Prospective Observational Study.Journal of asthma and allergy · 2026Article
- Finding trials for participants: an ethnographic study of successful recruitment strategies for clinical trials.Trials · 2025Article
- Taxonomy of chronic illness research recruitment: a restricted scoping review.BMC health services research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe ELCID Trial was a feasibility randomised controlled trial examining the effect on lung cancer diagnosis of lowering the threshold for referral for urgent chest X-ray for smokers and recent ex-smokers, aged over 60 with new chest symptoms. The qualitative component aimed to explore the feasibility of individually randomising patients to an urgent chest X-ray or not and to investigate any barriers to patient recruitment and participation. This would inform the design of any future definitive trial. This paper explores general practice staff insights into participating in and recruiting to diagnostic trials for possible/suspected lung cancer.
methodsQualitative interviews were conducted with 11 general practice staff which included general practitioners, a nurse practitioner, research nurses and practice managers. Interviews were analysed using a framework approach.
resultsFindings highlight general practice staff motivators to participate in the trial as recruiters, practice staff interactions with patients recruited onto the study, methods of organisation staff used to undertake the trial, the general impact of the trial on practice staff, how the trial research team supported the practices and lastly practice staff suggestions for trial delivery improvement.
conclusionsThe integration of a qualitative component focused on staff experiences participating in a lung diagnostic trial has demonstrated the feasibility to recruit for similar future studies within general practice. Although recruitment into trials can be difficult, results from our study offer suggestions on maximising patient recruitment not just to trials in general but also specifically for a lung diagnosis study.
trial registrationClinicalTrials.gov, NCT01344005. Registered on 27 April 2011.
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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.