Periodontitis and quality of life related to oral health in patients with systemic medical conditions: a systematic review and meta-analysis
Bibliographic record
Abstract
Measuring the proteins of individuals regarding their oral health allows a multidimensional analysis of different aspects of life and, therefore, a more comprehensive impact of oral conditions on quality of life, considering physical, social and emotional aspects, in addition to the environment and conditions health they live in. The aim of this study is to perform a systematic review to assess the association of periodontitis with oral health-related quality of life (OHRQoL) in adults with systemic medical conditions. The studies were selected from PubMed, EMBASE, LILACS, Web of Science, Scopus, and gray literature databases up to February 2023. Only observational studies with a clinical periodontal examination, diagnosis of periodontitis and the use of a validated instrument to determine OHRQoL were included. Two independent reviewers carried out the selection, data extraction, evaluation of the methodological quality of the studies (Newcastle-Ottawa scale) and certainty of evidence (GRADEpro). A total of thirteen studies comprising 1365 subjects, 92.3% of these using the OHIP-14 instrument were included. All studies showed moderate and high risk of bias and the certainty of the evidence was very low. The meta-analysis of studies of participants with Diabetes Mellitus, cardiovascular disease and psoriasis showed a strong impact of periodontitis on OHRQoL (standardized mean differences (SMD): 0.83; 95% CI: 0.20-1.47). Another metaanalysis of two studies in patients with chronic kidney disease showed a moderate effect (SMD: 0.46; 95% CI: 0.03-0.89). The association was not evident in more serious diseases such as leukemia. Subgroup analyzes and meta-regression showed that the type of comorbidity partly explains the heterogeneity between studies. It can be concluded that periodontitis has a small impact on OHRQoL in patients with systemic medical conditions. Associations were found only in chronic diseases that were more prevalent, but less physically and emotionally debilitating for patients. This study may help in the planning of preventive and therapeutic measures for periodontitis to improve oral health and quality of life, establishing strategies considering the different systemic diseases. However, the results still need to be interpreted with caution, being necessary in the future a larger sample size and high quality studies to confirm the findings.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.010 | 0.010 |
| 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".