+ HEALTH Aims Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiome…+ HEALTH Periapical lesions are challenging to detect on intraoral radiographs because of anatomical superimposition a…± HEALTH Ultrasound imaging is an indispensable diagnostic tool, yet its profound reliance on operator expertise inher…+ HEALTH Objectives We adapted the individualized polysocial risk score (iPsRS), a machine learning model originally d…± HEALTH Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei ( Bp ). Rapid diagno…+ HEALTH Classification is a core machine learning task, and ensemble voting methods are widely used to improve predic…+ CLIMATE Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools…± HEALTH Medicinal chemistry sits at the center of modern drug discovery, yet translating molecular designs into appro…
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TRV-2026-0016Version 2 · Revised

Written 2026-07-19 03:06:56 UTC · current record

Reason for this version

Corrected automated sector classification after taxonomy audit

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TRUVACE RECORD VERSION
record: TRV-2026-0016
version: 2
kind: revised
reason: Corrected automated sector classification after taxonomy audit
timestamp: 2026-07-19T03:06:56.002092Z
status: published
lens: general
sector: health
headline: 966-P: Real-World Impact of AI-driven CGM Platform on Glycemic Status in Type 2 Diabetes—A Retrospective Study
dek: Introduction and Objective: Artificial intelligence (AI)-powered diabetes management platforms integrating CGM technology represent a promising advancement in healthcare. This study evaluates the effectiveness of AI-integrated SDRMP platform in improving glycemic control.
 Methods: A 100-day retrospective study of 1752 individuals (1357 male, 395 female; mean age 50.22±11.33 years) assessed the...
gain_title: Introduction and Objective: Artificial intelligence (AI)-powered diabetes management platforms integrating CGM technology represent a promising advancement in healthcare. This study evaluates the effectiveness of AI-integrated SDRMP platform in improving glycemic control.
 Methods: A 100-day retrospective study of 1752 individuals (1357 male, 395 female; mean age 50.22±11.33 years) assessed the...
problem_title: (none)
trace_subject: (none)
gain_reading: Introduction and Objective: Artificial intelligence (AI)-powered diabetes management platforms integrating CGM technology represent a promising advancement in healthcare. This study evaluates the effectiveness of AI-integrated SDRMP platform in improving glycemic control.
 Methods: A 100-day retrospective study of 1752 individuals (1357 male, 395 female; mean age 50.22±11.33 years) assessed the...
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: Introduction and Objective: Artificial intelligence (AI)-powered diabetes management platforms integrating CGM technology represent a promising advancement in healthcare. This study evaluates the effectiveness of AI-integrated SDRMP platform in improving glycemic control.

Methods: A 100-day retrospective study of 1752 individuals (1357 male, 395 female; mean age 50.22±11.33 years) assessed the...
limitation: Machine-ingested summary: the claims above reflect a single primary source and have not been weighed against contradicting evidence by a Truvace editor yet.
tag: (none)
key_points: (none)
rundown: (none)
sources:
- peer_reviewed | Diabetes | https://doi.org/10.2337/db25-966-p | 2025-06-13
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