What Do Patients Prefer in Periodontal and Implant Therapy? A Scoping Review
Bibliographic record
Abstract
AIMS: Understanding and integrating patients' preferences into clinical practice can enhance personalized care, improve patient's adherence to treatment, and lead to better therapeutic outcomes. The aim of this scoping review was to map the existing literature investigating patients' preferences in periodontal and implant therapy while identifying key areas for future research and development. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) guidelines, an electronic search was conducted in four databases (PubMed, Google Scholar, Cochrane Library, and ScienceDirect) in July 2024 to identify studies evaluating patients' preferences for periodontal and implant therapy. RESULTS: The literature search yielded 384 studies, of which eight articles met the inclusion criteria. These studies were conducted between 2003 and 2019 in Brazil, China, Austria, Italy, Germany, Canada, USA, Chile, France, Spain, and Portugal. A total of 1642 patients were included. Preferences were assessed using various quantitative and mixed methodologies. Results indicate a strong preference for treatments aimed at preserving teeth, favoring conservative approaches. When teeth cannot be restored, most patients prefer an implant-supported fixed partial denture to avoid damaging adjacent teeth with a conventional tooth-supported fixed partial denture. In this context, treatment predictability is ranked as the most important factor. While no sociodemographic factors appeared to be associated with preferences in periodontal treatments, several predictors were identified for dental implant therapy. Younger patients, women, individuals with higher education levels, and those with high perceived dental health showed a higher willingness to pay for dental implants. CONCLUSION: The literature on patients' preferences in periodontal and implant therapies is scarce. Several trends are identified but further longitudinal studies are needed to explore patients' preferences over time and the role of sociodemographic and cultural segmentation criteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".