Evidence map›Paper›PMID 40568209›Full record

ArticleFrontiers in medicine2025

From data to medical context: the power of categorization in healthcare.

Batoul Msheik, Hamid Mcheick, Sara Hariri, Mehdi Adda, Mohamed Dbouk

Abstract read
In one paragraph

Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Batoul MsheikComputer Science Département, Université du Québec à Chicoutimi, Chicoutimi, QC, Canada.
Hamid McheickComputer Science Département, Université du Québec à Chicoutimi, Chicoutimi, QC, Canada.
Sara HaririComputer Science Département, Université du Québec à Chicoutimi, Chicoutimi, QC, Canada.
Mehdi AddaDépartement de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, Rimouski, QC, Canada.
Mohamed DboukComputer Science Department, Université Libanaise, Hadath, Lebanon.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the rapidly evolving healthcare domain, the ability to structure and interpret contextual medical data is crucial for delivering personalized and efficient patient care. While many existing studies attempt to define medical context through diverse categorizations, they often lack completeness or applicability in the real-world healthcare domain. This paper introduces a novel and comprehensive context categorization model composed of fifteen well-defined categories, bridging the gap between theoretical models and practical requirements in telemonitoring systems for chronic disease management. By incorporating important but often overlooked components such as social determinants, Service Level Agreements (SLAs), and environmental factors our model enhances clarity and strengthens decision-making in clinical settings. We validate the applicability of this framework through detailed case studies on asthma, COPD, and cardiovascular diseases, demonstrating its utility in enhancing telehealth solutions and aiding early intervention strategies.

Indexed as

chronic diseaseclinical supportcontext categorizationcontext of healthcare domaintelemedicine

Identifiers

PMID40568209
PMCPMC12187744

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.