ArticleJMIR research protocols2025
Improving the Reporting Quality of Studies on Information Extraction From Clinical Texts: Protocol for the Development of a Consensus-Based Reporting Guideline.
Article in JMIR research protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- The consensus-based CINEX guideline for reporting clinical information extraction studies.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Unlocking the potential of rice bean (Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Information extraction (IE) from clinical texts is increasingly important in health care; yet, reporting practices remain inconsistent. Existing guidelines do not fully address the unique challenges of IE studies. IE methods vary widely in their design, ranging from rule-based systems to advanced large language models, contributing to heterogeneity in reporting. While several reporting frameworks exist for applications of artificial intelligence in health care, they primarily focus on prediction modeling or clinical trials and associated protocols rather than text-based IE. Objective: This study aims to develop the Clinical Information Extraction (CINEX) guideline, a consensus-based reporting guideline for studies on clinical IE. Methods: The CINEX guideline is developed following an established guideline methodology, including a 3-round electronic Delphi (eDelphi) study with domain experts and a final in-person consensus meeting. The eDelphi process includes feedback loops and predefined consensus thresholds, with items rated on a 10-point scale for both relevance and maturity. The final consensus meeting is held as a hybrid workshop at the MEDINFO 2025 conference and focuses on finalizing the items that reached consensus. Results: Our results will provide a validated reporting guideline for studies on clinical IE. A preliminary set of 28 reporting items was drafted from a scoping review and existing frameworks. The draft guidelines include 5 key dimensions: information model, architecture, data, annotation, and outcome. This draft guideline will be refined through the eDelphi process. It is designed to be technology-agnostic and applicable across diverse IE approaches, including not only large language models but also traditional machine learning methods and rule-based and hybrid systems. Conclusions: The CINEX guideline provides structured, expert-validated guidance for reporting clinical IE studies, improving transparency, reproducibility, and comparability. The final guideline will be disseminated alongside an explanatory document to support adoption and implementation.
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.