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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA (lncRNA)...

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Related Experiment Video

Updated: Jun 5, 2026

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
07:53

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

Published on: October 13, 2023

Longitudinal Language-Model Reasoning Enables Automated Labeling of Lung Cancer Recurrence from Unstructured Clinical

Carlotta S Hoelzle1,2, Johannes Brandt2, Jonathan C Mueller1

  • 1Department of Radiology, Massachusetts General Hospital, Boston, United States of America.

Research Square
|June 4, 2026
PubMed
Summary

SCRIBE, a new framework, automates clinical endpoint extraction from patient records using AI. It improves accuracy and efficiency in identifying events like lung cancer recurrence.

Keywords:
Electronic Health RecordEndpoint ExtractionLarge Language ModelsReal World Evidence

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Oncology

Background:

  • Clinical endpoint extraction from unstructured patient narratives is labor-intensive.
  • Manual abstraction struggles with temporal precision and auditability.
  • Existing methods lack efficiency in processing large volumes of longitudinal data.

Purpose of the Study:

  • To introduce SCRIBE, an open-source, training-free framework for automated clinical label extraction.
  • To extract temporally precise and auditable clinical labels from unstructured text.
  • To improve the efficiency and accuracy of clinical endpoint identification.

Main Methods:

  • SCRIBE utilizes multi-stage large language model reasoning.
  • It reconciles longitudinal evidence for accurate event labeling and timing.
  • Verbatim evidence is linked to original source records for traceability.

Main Results:

  • SCRIBE achieved high lung cancer recurrence detection performance in a multi-center cohort.
  • Temporal localization error was halved compared to note-level inference.
  • Reduced multi-year patient documentation token volume by nearly two orders of magnitude.
  • 47.8% of false positives were identified as valid events missing from official registries.

Conclusions:

  • SCRIBE automates high-fidelity clinical endpoint extraction from unstructured records.
  • The framework enhances auditability and improves the completeness of clinical registries.
  • SCRIBE demonstrates significant potential for streamlining clinical research and data management.