Evidence map›Paper›PMID 36602707›Full record

ReviewClinical and experimental medicine2023

A review of research on eligibility criteria for clinical trials.

Qianmin Su, Gaoyi Cheng, Jihan Huang

Abstract readReview
In one paragraph

Review in Clinical and experimental medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. A Multi-Model LLM Consensus Framework to Identify EHR-Predictable Eligibility Criteria in NSCLC Immunotherapy Trials.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    Article
  4. KRAS Clinical Trial Finder: A Patient-Centered Platform for Improving Access to Clinical Trial Information.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    Article
  5. Article
  6. Review
  7. Article
  8. Analysis of eligibility criteria clusters based on large language models for clinical trial design.Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
  9. Review
  10. Review
  11. Article
  12. Article
  13. 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

3 authors.

Qianmin SuDepartment of Computer Science, School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, No. 333 Longteng Road, Shanghai, 201620, China. suqm@sues.edu.cn.
Gaoyi ChengDepartment of Computer Science, School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, No. 333 Longteng Road, Shanghai, 201620, China.
Jihan HuangCenter for Drug Clinical Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are important prerequisites for the success of clinical trials. It directly affects the final results of the clinical trials. Inappropriate eligibility criteria will lead to insufficient recruitment, which is an important reason for the eventual failure of many clinical trials. We have investigated the research status of eligibility criteria for clinical trials on academic platforms such as arXiv and NIH. We have classified and sorted out all the papers we found, so that readers can understand the frontier research in this field. Eligibility criteria are the most important part of a clinical trial study. The ultimate goal of research in this field is to formulate more scientific and reasonable eligibility criteria and speed up the clinical trial process. The global research on the eligibility criteria of clinical trials is mainly divided into four main aspects: natural language processing, patient pre-screening, standard evaluation, and clinical trial query. Compared with the past, people are now using new technologies to study eligibility criteria from a new perspective (big data). In the research process, complex disease concepts, how to choose a suitable dataset, how to prove the validity and scientific of the research results, are challenges faced by researchers (especially for computer-related researchers). Future research will focus on the selection and improvement of artificial intelligence algorithms related to clinical trials and related practical applications such as databases, knowledge graphs, and dictionaries.

Indexed as

Artificial IntelligenceResearch DesignClinical Trials as TopicEligibility DeterminationHumansNatural Language ProcessingPatient SelectionArtificial intelligenceBig dataClinical trialExclusion criteriaInclusion criteriaMachine learning

Identifiers

PMID36602707
PMCPMC9815064

What OpenQuestion holds

Textmetadata
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.