ArticleFrontiers in public health2025
Development of a nomogram model for predicting dementia risk in the older adult population of Weifang, Shandong Province, China: based on the biopsychosocial model.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Background: Dementia has emerged as a predominant health challenge. However, there is a notable research gap in the collective screening of dementia risks. Hence, there is a pressing need to formulate a dementia prediction tool tailored to the older adult demographic, enabling the identification of high-risk individuals for dementia. Methods: From May to October 2023, a multi-stage sampling method was utilized to survey older adult individuals aged 60 and above in Weifang. This study employed the Brief Community Screening Instrument for Dementia (BCSI-D) for the identification of individuals with dementia. We integrated the biopsychosocial model to construct a comprehensive pool of factors influencing dementia. Employing the least absolute shrinkage and selection operator and multivariate logistic regression analyses, independent influencing factors were identified to construct a nomogram prediction model. Results: Six hundred and sixty valid questionnaires were included in the final analysis, with a validity rate of 95.23%. We identified 178 cases of dementia using the BCSI-D. Napping, lack of concentration, self-assessed health status, education level, residence, social interaction and medical insurance were independent influencing factors for dementia. The efficiency analysis of the prediction model, constructed using these factors, demonstrated area under the receiver operating characteristic of 0.751 for the training set and 0.794 for the test set. The decision curve analysis threshold probabilities for the training and test sets were 5-60% and 1-60%, respectively. The calibration curves of both datasets exhibited a high degree of fitting with the predicted curve. Conclusion: We developed a dementia risk identification model with noteworthy predictive performance. The proposed model offers theoretical and data support for collective dementia screening.
Indexed as
Identifiers
What Socratic 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 Socratic graph.