Does Bruxism Contribute to Dental Implant Failure? A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Bruxism was usually considered as a contraindication for oral implanting. The causal relationship between bruxism and dental implant failure was remained controversial in existing literatures. PURPOSE: This meta-analysis was performed to investigate the relationship between them. MATERIALS AND METHODS: This review conducted an electronic systematic literature search in MEDLINE (PubMed) and EmBase in November 2013 without time and language restrictions. Meanwhile, a hand searching for all the relevant references of included studies was also conducted. Study information extraction and methodological quality assessments were accomplished by two reviewers independently. A discussion ensued if any disagreement occurred, and unresolved issues were solved by consulting a third reviewer. Methodological quality was assessed by using the Newcastle-Ottawa Scale tool. Odds ratio (OR) with 95% confidence interval (CI) was pooled to estimate the relative effect of bruxism on dental implant failures. Fixed effects model was used initially; if the heterogeneity was high, random effects model was chosen for meta-analysis. Statistical analyses were carried out by using Review Manager 5.1. RESULTS: In this meta-analysis review, extracted data were classified into two groups based on different units. Units were based on the number of prostheses (group A) and the number of patients (group B). In group A, the total pooled OR of bruxers versus nonbruxers for all subgroups was 4.72 (95% CI: 2.66-8.36, p = .07). In group B, the total pooled OR of bruxers versus nonbruxers for all subgroups was 3.83 (95% CI: 2.12-6.94, p = .22). CONCLUSIONS: This meta-analysis was performed to evaluate the relationship between bruxism and dental implant failure. In contrast to nonbruxers, prostheses in bruxers had a higher failure rate. It suggests that bruxism is a contributing factor of causing the occurrence of dental implant technical/biological complications and plays a role in dental implant failure.
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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 | 0.025 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.059 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".