Is Bruxism a Risk Factor for Dental Implants? A Systematic Review of the Literature
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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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".