Natural language processing to identify substance misuse in the electronic health record.

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

4 citing papers in PubMed.

1. Enhancing substance use detection in clinical notes with large language models.Drug and alcohol dependence · 2025
   Article
2. An Extraction Tool for Venous Thromboembolism Symptom Identification in Primary Care Notes to Facilitate Electronic Clinical Quality Measure Reporting: Algorithm Development and Validation Study.JMIR medical informatics · 2025
   Article
3. Enhancing Substance Use Detection in Clinical Notes with Large Language Models.Research square · 2025
   Article
4. Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts.Findings of ACL. NAACL · 2024
   Article

Corrections and comments

• Comment on
  Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study.2022

Authors and funding

2 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

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