Evidence map›Paper›PMID 40630805›Full record

ArticleTelemedicine reports2025

Demographics, Comorbidities, and Care-Seeking Intent Among Individuals with Obesity or Overweight Status Using Outpatient AI-Based Virtual Triage.

George A Gellert, Anna Nowicka, Maria Marecka, Gabriel L Gellert, Tim Price

Abstract read
In one paragraph

Article in Telemedicine reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

George A GellertInfermedica, San Antonio, Texas, USA.ORCID https://orcid.org/0000-0002-3519-7486
Anna NowickaInfermedica, Wroclaw, Poland.
Maria MareckaInfermedica, San Antonio, Texas, USA.
Gabriel L GellertInfermedica, San Antonio, Texas, USA.
Tim PriceInfermedica, Wroclaw, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Compared with persons with normal body mass index (BMI), examine the profile and health care-seeking intent of individuals with obesity/overweight status engaging outpatient artificial intelligence-based virtual triage and care referral (VTCR). Methods: VTCR encounters of patients with high and normal BMI were compared over a 56-month period to assess differences in demographics, clinical risks, symptoms, conditions, triage recommendations, and care intent. Results: In 7,222,363 encounters, 29.6% of patients reported having obesity/overweight status, increasing with age and peaking at 45-59 years (46.4%). Mean age for the high BMI group was 35.2 years and 28.7 years in the normal BMI group. Patients with obesity/overweight status reported noncommunicable diseases twice as frequently, including hypertension (relative risk [RR] 2.6), hypercholesterolemia (RR 2.4), diabetes mellitus (RR 2.4), and asthma (RR 1.4) ( Conclusions: VTCR effectively identified individuals with high BMI and their associated comorbidities. The results suggest that patients with obesity/overweight status utilize health care services at higher rates. VTCR holds promise as a valuable patient engagement, screening, early diagnosis, and health monitoring tool in managing obesity/overweight status in populations.

Indexed as

artificial intelligencedigital triageobesityoverweightsymptom checkertelemedicinevirtual health carevirtual triage and care referral

Identifiers

PMID40630805
PMCPMC12235118

What OpenQuestion holds

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Registered trials

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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.