History of periodontitis as a risk factor for implant failure and incidence of peri‐implantitis: A systematic review, meta‐analysis, and trial sequential analysis of prospective cohort studies
Bibliographic record
Abstract
INTRODUCTION: Dental implants are widely employed as dependable replacements for lost teeth. However, it is crucial to establish, solely through prospective cohort studies, whether a history of periodontitis indeed constitutes a significant risk factor for implant failure. METHODS: A systematic literature search was conducted in October 2022 in several electronic databases with subsequent manual updates. Only original prospective cohort studies evaluating the implant (loss) rate ≥1 year after implant loading were included. Logarithmic risk ratio and weighted mean differences were calculated. Study results were summarized using random effects meta-analyses evaluated by trial sequential analyses. The Newcastle-Ottawa scale evaluated study bias and the GRADE approach assessed the certainty/quality of the evidence. RESULTS: A total of 14 publications reporting on 12 prospective cohort studies were included. Low evidence certainty/quality evidence due to the absence of randomized clinical trials revealed significantly greater odds of failure in patients with a history of periodontitis at follow-ups both after ≤5 years (RR = 1.62; 95% CI: 1.71-2.37; p = 0.013) and >5 years (RR = 2.26; 95% CI: 1.12-4.53; p = 0.023). The incidence of peri-implantitis (RR = 4.09; 95% CI: 1.93-8.58; p < 0.001) and the weighted mean (WM) of marginal bone loss (WM difference = 0.75 mm; 95% CI: 0.18-1.31; p < 0.05) were statistically significantly greater in the periodontally compromised group, whereas there was no significant difference between the two groups for peri-implant probing depth. CONCLUSION: A history of periodontitis can be considered a significant risk factor for incident implant failure, peri-implantitis, and greater marginal bone loss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".