Death by a Thousand Screens: A Practical Role for Machine Learning in Emergency Medicine.

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

3 citing papers in PubMed.

1. Machine learning models to detect opioid misuse in emergency department patients at triage.The American journal of emergency medicine · 2026
   Article
2. Development and evaluation of machine learning models for the detection of emergency department patients with opioid misuse from clinical notes.JAMIA open · 2026
   Article
3. Machine learning models to detect opioid misuse in Emergency Department patients at triage.medRxiv : the preprint server for health sciences · 2025
   Article

Corrections and comments

• Commented on by
  In reply.2023
• Comment on
  Principles of Screening for Disease and Health Risk Factors in the Emergency Department.2023

Authors and funding

1 author.

Funding

Abstract

PubMed holds no abstract for this paper.

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