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Record W4408246736 · doi:10.1038/s41746-025-01549-7

A mobile health application that supports a patient centered approach to cardiovascular risk management

2025· editorial· en· W4408246736 on OpenAlexaff
Ben Li, Kimia Heydari, Elizabeth J. Enichen, Joseph C. Kvedar

Bibliographic record

Venuenpj Digital Medicine · 2025
Typeeditorial
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsArtificial Intelligence in Medicine (Canada)University of Toronto
Fundersnot available
KeywordsRisk managementCardiovascular healthHealth management systemMedicineComputer scienceBusinessRisk analysis (engineering)Internal medicineAlternative medicinePathologyDisease

Abstract

fetched live from OpenAlex

Digital health tools have the potential to support patients in managing their chronic diseases. Recently, Ullrich and colleagues (2025) introduced PreventiPlaque, a mobile health application that provides patients with up-to-date ultrasound images of their carotid plaques and tracks their lifestyle habits. Through a randomized controlled trial, the authors provide evidence of PreventiPlaque’s efficacy in improving patients’ cardiovascular risk profiles. This study highlights the potential for digital health interventions to provide personalized health information to patients and empower them to take actionable steps to improve their cardiovascular health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.325
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.363
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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