ArticleJAMIA open2024
Validation and clinical discovery demonstration of breast cancer data from a real-world data extraction platform.
Article in JAMIA open, 2024. 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.
- Article
- Development and Assessment of a Pipeline for Extracting Structured Data From Free-Text Medical Reports Using a Large Language Model.JCO clinical cancer informatics · 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To validate and demonstrate the clinical discovery utility of a novel patient-mediated, medical record collection and data extraction platform developed to improve access and utilization of real-world clinical data. Materials and Methods: Clinical variables were extracted from the medical records of 1011 consented patients with breast cancer. To validate the extracted data, case report forms completed using the structured data output of the platform were compared to manual chart review for 50 randomly-selected patients with metastatic breast cancer. To demonstrate the platform's clinical discovery utility, we identified 194 patients with early-stage clinical data who went on to develop distant metastases and utilized the platform-extracted data to assess associations between time to distant metastasis (TDM) and early-stage tumor histology, molecular type, and germline Results: The platform-extracted data for the validation cohort had 97.6% precision (91.98%-100% by variable type) and 81.48% recall (58.15%-95.00% by variable type) compared to manual chart review. In our discovery cohort, the shortest TDM was significantly associated with metaplastic (739.0 days) and inflammatory histologies (1005.8 days), HR-/HER2- molecular types (1187.4 days), and positive Discussion: The precision and recall of platform-extracted clinical data are reported, although specificity could not be assessed. The data can generate clinically-relevant insights. Conclusion: The structured real-world data produced by a novel patient-mediated, medical record-extraction platform are reliable and can power clinical discovery.
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.