Is Bruxism a Risk Factor for Dental Implants? A Systematic Review of the Literature
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To systematically review the literature on the role of bruxism as a risk factor for the different complications on dental implant-supported rehabilitations. MATERIAL AND METHODS: A systematic search in the National Library of Medicine's Medline Database was performed to identify all peer-reviewed papers in the English literature assessing the role of bruxism, as diagnosed with any other diagnostic approach (i.e., clinical assessment, questionnaires, interviews, polysomnography, and electromyography), as a risk factor for biological (i.e., implant failure, implant mobility, and marginal bone loss) or mechanical (i.e., complications or failures of either prefabricated components or laboratory-fabricated suprastructures) complications on dental implant-supported rehabilitations. The selected articles were reviewed according to a structured summary of the articles in relation to four main issues, viz., "P" - patients/problem/population, "I" - intervention, "C" - comparison, and "O" - outcome. RESULTS: A total of 21 papers were included in the review and split into those assessing biological complications (n = 14) and those reporting mechanical complications (n = 7). In general, the specificity of the literature for bruxism diagnosis and for the study of the bruxism's effects on dental implants was low. From a biological viewpoint, bruxism was not related with implant failures in six papers, while results from the remaining eight studies did not allow drawing conclusions. As for mechanical complications, four of the seven studies yielded a positive relationship with bruxism. CONCLUSIONS: Bruxism is unlikely to be a risk factor for biological complications around dental implants, while there are some suggestions that it may be a risk factor for mechanical complications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it