Risk of early implant failure in grafted and non-grafted sites: A systematic review and meta-analysis.
Bibliographic record
Abstract
PURPOSE: To assess whether bone grafting is associated with early implant failure (defined as a lack of osseointegration detected prior to functional loading) and to evaluate the association between bone grafting procedures and other risk factors for early implant failure. MATERIALS AND METHODS: Two independent reviewers conducted an electronic search of MEDLINE (via PubMed). Meta-analysis was performed for the odds ratio of bone grafting procedures associated with early implant failure. The Newcastle-Ottawa Scale for cohort studies was used to assess the risk of bias. RESULTS: Of the 231 articles selected for full-text review, 10 were included in the qualitative analysis and for quantitative meta-analysis. An odds ratio of 1.50 (95% confidence interval 1.06-2.13) was recorded for bone grafting procedures associated with early implant failure. Data regarding the association of bone grafting and other risk factors in determining early implant failure were insufficient for quantitative analysis. CONCLUSIONS: Within the limitations of this study, a significant positive association was found between bone grafting procedures and early implant failure. The possible negative effect of bone grafting procedures on implant osseointegration should be considered when planning implant therapy.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".