Evidence map›Paper›PMID 41276591›Full record

ArticleScientific reports2025

Entropy removal of clinical features.

Kian D Samadian, Emma Chua, Boyu Peng, Adriana Coleska, Ahmad Hassan, Paul Chong, Brian Locke, David M Liebovitz, Cory Rohlfsen, Shuhan He

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Kian D SamadianDepartment of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St, Boston, MA, 02114, USA. ksamadian@mgb.org.
Emma ChuaPasadena City College, Pasadena, CA, USA.
Boyu PengConductScience, Skokie, IL, USA.
Adriana ColeskaDepartment of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St, Boston, MA, 02114, USA.
Ahmad HassanDepartment of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St, Boston, MA, 02114, USA.
Paul ChongDepartment of Ophthalmology, Walter Reed Medical Center, Bethesda, MD, USA.
Brian LockeDepartment of Pulmonary and Critical Care, Intermountain Medical Center, Murray, UT, USA.
David M LiebovitzFeinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Cory RohlfsenDepartment of General Internal Medicine, University of Nebraska Medical Center, Omaha, NE, USA.
Shuhan HeDepartment of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St, Boston, MA, 02114, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interpreting clinical findings is fundamental to diagnosis and care. However, the contribution of individual features to reducing diagnostic uncertainty remains unclear. Information theory's Shannon entropy offers a way to quantify how much a finding narrows diagnostic possibilities. We analyzed 405 symptoms, physical signs, demographic factors, and tests drawn from 23 reviews to calculate entropy reduction from diagnostic tables and compared them to established accuracy measures, including Youden's index and predictive values. Most features yielded modest uncertainty reductions, with nearly half removing less than one-fifth of uncertainty, while a subset of high-performance findings reduced uncertainty by more than 40%. Entropy reduction correlated strongly with Youden's index and positive predictive value. Entropy analysis may enhance evaluation by highlighting features that offer greater informational benefit.

Indexed as

EntropyHumansInformation TheoryUncertaintyClinical decision-makingDiagnostic performanceDiagnostic uncertaintyInformation theoryShannon entropyYouden’s index

Identifiers

PMID41276591
PMCPMC12749281

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.