ArticleJournal of oral rehabilitation2025
Ontology and Bruxism: Do We Have Enough Information?
Article in Journal of oral rehabilitation, 2025. 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.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The idea of classifying and defining bruxism according to ontological principles may be interesting, but currently we just do not have enough information to label in a black or white manner the many facets of bruxism. In an era in which general knowledge on bruxism by the dental communities is surely in need of improvement, efforts to clarify the road map tracked by the current panelists who drafted the definition should be appraised carefully. The recent introduction of a standardized multidimensional evaluation system (i.e., Standardized Tool for the Assessment of Bruxism [STAB]) and a screening instrument for bruxism (i.e., BruxScreen) should be viewed as the starting points to enter a new era in the discipline of bruxism, in which non-hierarchical and non-preconceived approaches are used to collect data. Artificial intelligence strategies to mine data gathered with the above instruments might help building predictive models along the etiology-status-consequences trajectory, as recently suggested in a model for awake bruxism metrics. Until then, proposals to adopt ontological principles to classify bruxism will be merely based on speculations rather than on facts.
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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.