Prevalence of Bruxism in Children and Adolescents with Cerebral Palsy: Systematic Review and Meta-analysis
Bibliographic record
Abstract
AIMS: To perform a systematic review and meta-analysis compiling data on the prevalence of bruxism in children and adolescents with cerebral palsy. METHODS AND RESULTS: Searches were carried out in PubMed/Medline, Web of Science, and Scopus databases to identify the articles published by February 2023. Two independent reviewers, and in duplicate, employed a two-stage process to select publications. The same two reviewers performed the data extraction. Studies were included when the following eligibility criteria were met: performed in children and/or adolescents with cerebral palsy and reporting bruxism. Potentially eligible studies were read in full and excluded that: not presented numerical data on the prevalence of bruxism; not reported how the bruxism was assessed; not reported data about the cerebral palsy; and not an observational study. The risk assessment of bias was assessed by the Newcastle- Ottawa Scale. After reading the titles and abstracts of the 358 identified articles, eight articles from 1966 to 2020 were included. The main reason for not including the studies was not to report data about bruxism (59.3%), and 44.5% were excluded for not reporting data from patients with cerebral palsy. The studies were carried out in schools, university hospitals, or centers for patients with special needs (Brazil, the United States, and Egypt). The pooled prevalence of bruxism in children and adolescents with cerebral palsy was 46% (95%CI: 0.38-0.55) after removing one study. CONCLUSION: The pooled prevalence of bruxism in children with cerebral palsy can be considered high since almost half of the studied population is affected by this condition. PROSPERO #CRD42021225781.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.043 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".