Impact of Guided Implant Dentistry on Patient Quality of Life, Satisfaction, and Psychological Well-Being: A Systematic Review
Bibliographic record
Abstract
Introduction: Oral implantology, a modern approach to rehabilitating edentulous patients, has advanced significantly with digital technologies, notably computer-guided surgery. This technique is considered precise and predictable. However, it is essential to assess this technique from the patient’s perspective, focusing on its impact on quality of life and satisfaction. Methods: A literature search was conducted in PubMed, Embase, and CINAHL up to January 2025. Clinical trials and case series studies were included. Studies conducted on partially or fully edentulous patients were selected for inclusion. The studies included static or dynamic guided oral implant treatments, as well as conventional treatments, and evaluated patient-reported outcomes, specifically perceived satisfaction and quality of life. A qualitative synthesis of the findings was performed, and the quality of the included studies was assessed using the Newcastle–Ottawa Scale (NOS). Results: A total of twelve studies were included. The most commonly used questionnaires for evaluation were the Visual Analog Scale (VAS), Oral Health-Related Quality of Life (OHQoL), and Oral Health Impact Profile (OHIP). Computer-guided implantology appears to be a valid and predictable technique for dental implant placement. It is associated with a reduced intraoperative and postoperative pain. Some studies, however, did not identify significant differences compared with conventional implant surgery. Conclusions: Guided oral implantology is a viable option for oral rehabilitation in edentulous patients, offering benefits in surgical precision, pain reduction, and patient experience. Its effects on surgical time and overall patient satisfaction, however, warrant further investigation.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| 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) | 0.004 | 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".