ArticleScientific reports2024
Text classification models for assessing the completeness of randomized controlled trial publications based on CONSORT reporting guidelines.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses 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, 3 syntheses or guidelines pooled it.
- Preferences for AI-Enabled Health Care Technologies: Systematic Review of Discrete Choice Experiments and Reporting Quality Assessment Using the DIRECT Checklist.Journal of medical Internet research · 2026Pooled it
- Large Language Model Analysis of Reporting Quality of Randomized Clinical Trial Articles: A Systematic Review.JAMA network open · 2025Pooled it
- Data extraction methods for systematic review (semi)automation: Update of a living systematic review.F1000Research · 2021Pooled it
- 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
- Navigating blinding challenges in complex intervention trials: insights from a UK researcher survey.Trials · 2025Article
- 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
- SPIRIT-CONSORT-TM: a corpus for assessing transparency of clinical trial protocol and results publications.Scientific data · 2025Article
- SPIRIT-CONSORT-TM: a corpus for assessing transparency of clinical trial protocol and results publications.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
- Update of
Authors and funding
5 authors.
Funding
Abstract
Complete and transparent reporting of randomized controlled trial publications (RCTs) is essential for assessing their credibility. We aimed to develop text classification models for determining whether RCT publications report CONSORT checklist items. Using a corpus annotated with 37 fine-grained CONSORT items, we trained sentence classification models (PubMedBERT fine-tuning, BioGPT fine-tuning, and in-context learning with GPT-4) and compared their performance. We assessed the impact of data augmentation methods (Easy Data Augmentation (EDA), UMLS-EDA, text generation and rephrasing with GPT-4) on model performance. We also fine-tuned section-specific PubMedBERT models (e.g., Methods) to evaluate whether they could improve performance compared to the single full model. We performed 5-fold cross-validation and report precision, recall, F
Indexed as
Identifiers
What Socratic 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 Socratic graph.