ArticleAnnals of medicine2026
Machine learning for screening laryngopharyngeal reflux symptoms in college students: a cross-sectional study.
Article in Annals of medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Global burden of Laryngopharyngeal reflux disease: a systematic review of epidemiology, cost-burden and awareness.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
bckgroundLaryngopharyngeal reflux (LPR) is a widespread global health issue. Its recurring symptoms and impact on quality of life create significant economic burdens for individuals and society. To examine the links between lifestyle, diet, and LPR symptoms (LPRS) in college students, and to build an LPRS screening model using a Genetic Algorithm (GA)-Stacking method. PATIENTS AND
methodsA cross-sectional study of 502 undergraduates from 21 universities in Jilin Province, China, using an electronic questionnaire. LPRS were assessed via the Reflux Symptom Index (RSI). Associations were analyzed with multiple methods, and a GA-Stacking screening model was developed.
resultsLPRS prevalence was 50.20% (252/502). Significant risk factors included frequent fried food consumption (OR: 1.89; 95% CI, 1.35-2.64), late-evening meals (OR: 2.15; 95% CI, 1.54-3.01), and low physical activity (OR: 1.72; 95% CI, 1.23-2.41). The GA-Stacking model performed well, with a recall of 0.909, accuracy of 0.927, and AUC of 0.96 (95% CI, 0.94-0.98).
conclusionsModifiable factors like fried food intake and meal timing are strongly linked to LPRS in students. The GA-Stacking model effectively identifies high-risk individuals for early intervention, highlighting the role of lifestyle changes and informing targeted health strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.