Association Between Proton Pump Inhibitor Use and the Severity of Periodontal Disease and Peri-Implantitis: A Systematic Review
Bibliographic record
Abstract
This systematic review investigates the probable effect of proton pump inhibitor (PPI) use on the severity of periodontal disease and peri-implantitis and implant survival. We conducted a literature search in PubMed, Scopus, and Cochrane Central Library up to April 2024. Two review authors independently screened the title and abstracts and then the full texts of retrieved studies. Observational and clinical trial studies that assessed the association between PPI use and periodontal disease severity and peri-implantitis or implant survival were included. Data extraction from the included studies was done by 2 reviewers independently. Of 940 studies initially retrieved from online searching, 7 met the inclusion criteria. Three studies examined periodontitis, whereas 4 focused on peri-implantitis and implant longevity. On the contrary, evidence regarding the impact of PPI use on peri-implantitis and implant survival is conflicting. Therefore, more well-designed randomized controlled trials are warranted to come to a definite conclusion. Because PPIs alter the gut microbiome and affect bone, plus that the pathogenesis and etiology of periodontal disease are affected by bacteria within the periodontal pocket, it is hypothesized that they may affect periodontal pathogenesis.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".