ArticleJournal of oncology practice2013
Screening intervention to identify eligible patients and improve accrual to phase II-IV oncology clinical trials.
Article in Journal of oncology practice, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review on natural language processing systems for eligibility prescreening in clinical research.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026Review
- Article
- AI-Driven Patient Screening for Clinical Trials in Pancreatic Cancer: The PANCR-AI Pilot Retrospective Comparative Study.JMIR cancer · 2026Article
- New strategies for enhancing enrollment of underrepresented minorities in lymphoma clinical trials.Blood advances · 2025Review
- Impact of an Oncology Clinical Pharmacist Intervention on Clinical Trial Enrollment in The US Oncology Network's MYLUNG Consortium.Journal of the advanced practitioner in oncology · 2024Article
- Strategies for increasing accrual in cancer clinical trials: What is the evidence?Cancer medicine · 2024Review
- Evaluation of an artificial intelligence-based clinical trial matching system in Chinese patients with hepatocellular carcinoma: a retrospective study.BMC cancer · 2024Article
- Brief Report: Implementation of a Universal Prescreening Protocol to Increase Recruitment to Lung Cancer Studies at a Veterans Affairs Cancer Center.JTO clinical and research reports · 2022Article
- The Impact of an Online Training Program About Cancer Clinical Trials on Primary Care Physicians' Knowledge, Attitudes and Beliefs, and Behavior.Journal of cancer education : the official journal of the American Association for Cancer Education · 2021Article
- Accuracy of an Artificial Intelligence System for Cancer Clinical Trial Eligibility Screening: Retrospective Pilot Study.JMIR medical informatics · 2021Article
- Strategies designed to help healthcare professionals to recruit participants to research studies.The Cochrane database of systematic reviews · 2016Review
- Paving the road to clinical trial participation: removing road blocks and directing patients toward novel therapies.Journal of the National Comprehensive Cancer Network : JNCCN · 2015Article
- The National Cancer Institute-American Society of Clinical Oncology Cancer Trial Accrual Symposium: summary and recommendations.Journal of oncology practice · 2013Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeLow enrolment rates in clinical trials present a barrier to the development of novel cancer therapies. Currently, only 3% of patients with cancer participate, and many studies fail to achieve necessary enrolment. The objective of this study was to evaluate whether a screening intervention to identify potentially eligible patients (PEPs) would increase accrual rates. PATIENTS AND
methodsOver a 4-month intervention period, PEPs for 21 phase II-IV breast, gastrointestinal, genitourinary, gynecology, and lung cancer trials were identified by a screening coordinator. This individual reviewed the electronic medical records of patients attending outpatient clinics and flagged PEPs for 10 medical oncologists at the BC Cancer Agency. Patients who were already documented to be trial eligible by physicians were not flagged. Oncologists were surveyed regarding the helpfulness and accuracy of the intervention.
resultsDuring the intervention period, 73 patients were enrolled, compared with 61 patients enrolled in the 4 months prior and 51 patients in the 4 months after. A total of 2,098 charts were reviewed, and 120 PEPs were identified during the intervention period, resulting in 19 PEPs who enrolled and four PEPs who declined a clinical trial. Relative accrual rates adjusted for oncologist appointments were 0.85 (P = .15) before and 0.70 (P < .005) after, relative to the intervention period. Oncologist-returned surveys indicated that 67% of flags were helpful, and 70% were accurate.
conclusionsIn this study, manually screening patient records increased enrolment to specific clinical trials. A screening intervention process, involving a dedicated screening coordinator, should be considered to improve clinical trial accrual.
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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.