ArticleJAMA network open2020
Development and Validation of a Natural Language Processing Tool to Generate the CONSORT Reporting Checklist for Randomized Clinical Trials.
Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large Language Model Analysis of Reporting Quality of Randomized Clinical Trial Articles: A Systematic Review.JAMA network open · 2025Pooled it
- The Effect of an Abdominal Binder on Postoperative Pain After Laparoscopic Incisional Hernia Repair–A Multicenter, Randomized Pilot Trial (ABIHR-I) of the Intraperitoneal Onlay-Mesh Technique.Deutsches Arzteblatt international · 2021Trial
- SPIRIT-CONSORT-ELM: Element-Level Annotated Dataset and Large Language Model Approach for Assessing Randomized Controlled Trial Reporting.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Using Large Language Models to Assess the Consistency of Randomized Controlled Trials on AI Interventions With CONSORT-AI: Cross-Sectional Survey.Journal of medical Internet research · 2025Article
- RAPID: Reliable and efficient Automatic generation of submission rePortIng checklists with large language moDels.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- GPT for RCTs? Using AI to determine adherence to clinical trial reporting guidelines.BMJ open · 2025Article
- Polypropylene vs. stainless-steel wire suture: short-term recurrence rate after shouldice primary inguinal hernia repair, a non-inferior analysis among 1120 patients. A case-control study.Hernia : the journal of hernias and abdominal wall surgery · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Importance: Adherence to the Consolidated Standards of Reporting Trials (CONSORT) for randomized clinical trials is associated with improvingquality because inadequate reporting in randomized clinical trials may complicate the interpretation and the application of findings to clinical care. Objective: To evaluate an automated reporting checklist generation tool that uses natural language processing (NLP), called CONSORT-NLP. Design, Setting, and Participants: This study used published journal articles as training, testing, and validation sets to develop, refine, and evaluate the CONSORT-NLP tool. Articles reporting randomized clinical trials were selected from 25 high-impact-factor journals under the following categories: (1) general and internal medicine, (2) oncology, and (3) cardiac and cardiovascular systems. Main Outcomes and Measures: For an evaluation of the performance of this tool, an accuracy metric defined as the number of correct assessments divided by all assessments was calculated. Results: The CONSORT-NLP tool uses the widely used Portable Document Format as an input file. Of the 37 CONSORT reporting items, 34 (92%) were included in the tool. Of these 34 reporting items, 30 were fully implemented; 28 (93%) of the fully implemented CONSORT reporting items had an accuracy of more than 90% for the validation set. The remaining 2 (7%) had an accuracy between 80% and 90% for the validation set. Two to 5 articles were selected from each of these journals for a total of 158 articles to establish a training set of 111 articles to train CONSORT-NLP for CONSORT reporting items, a testing set of 25 articles to refine CONSORT-NLP, and a validation set of 22 articles to assess the performance of CONSORT-NLP. The CONSORT-NLP tool used the Portable Document Format of the articles as input files. A CONSORT-NLP graphical user interface was built using Java in 2019. The time required to complete the CONSORT checklist manually vs using the CONSORT-NLP tool was compared for 30 articles. Two case studies for randomized clinical trials are provided as an illustration for the CONSORT-NLP tool. For the 30 articles investigated, CONSORT-NLP required a mean (SD) 23.0 (4.1) seconds, whereas the manual reviewer required a mean (SD) 11.9 (2.2), 22.6 (4.6), and 57.6 (7.1) minutes, for 3 reviewers, respectively. Conclusions and Relevance: The CONSORT-NLP tool is designed to assist in the reporting of randomized clinical trials. Potential users of CONSORT-NLP include clinicians, researchers, and scientists who plan to publish a randomized trial study in a peer-reviewed journal. The use of CONSORT-NLP may help them save substantial time when generating the CONSORT checklist. This tool may also be useful for manuscript reviewers and journal editors who review these articles.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.