ReviewOrthopadie (Heidelberg, Germany)2026
[LLM-based extraction of clinical data: potentials and challenges].
Review in Orthopadie (Heidelberg, Germany), 2026. 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.
- Observational
- DataXflowGen for GenAI-driven model generation.Scientific reports · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLarge language models (LLM) can automatically process clinical free-text documents, extract key information, and thereby reduce reading effort and documentation-related workload. High-quality data and targeted model control are essential for practical applicability. MATERIAL AND
methodsVarious approaches to information extraction are presented. Additionally, 24 unstructured pathological reports of bone and soft tissue tumors are processed using the local, generic LLM Llama 4 Scout with three different prompt variants and compared in terms of extraction quality.
resultsPrompt design had a substantial impact on model behavior. Prompts with clear parameter definitions and examples achieved the most reliable results. Typical LLM-specific errors, such as hallucinations and misclassifications, were also observed. LLM can support clinical staff by rapidly and systematically extracting relevant content from free-text documents. Safe and effective use requires high-quality data, precise inputs, and close collaboration between medical and technical experts.
Indexed as
Identifiers
41417114What 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.