<b>PRISMA-P-checklist for i</b><b>mpact of haptic simulator in prosthodontics training during preclinic dental education: a systematic review protocol</b>
Bibliographic record
Abstract
BackgroundProsthodontics significantly impacts oral health-related quality of life, especially oral comfort, functions, aesthetics, and overall well-being. This discipline is grounded in a solid academic and evidence-based understanding of fundamental principles for managing dental diseases and aims to restore the health of teeth and supporting tissues and compensate missing ones. Dental education is evolving quickly with the rise of digital dentistry, especially in prosthodontics. Integrating augmented reality simulations and haptic feedback has significantly advanced this transformation. This systematic review protocol intends to determine the effectiveness of haptic simulators in prosthodontics training during preclinical dental education.MethodsAn exhaustive search strategy will be employed, exploring PubMed, Scopus, EBSCO, Web of Science and Cochrane Central to select relevant studies, thereby enhancing the robustness of the review findings. Boolean operators (AND,OR) were utilized to assemble MeSH terms and relevant keywords. Titles and abstracts screening to identify studies that satisfy the eligibility criteria was followed by the quality and risk of bias assessment for the selected studies, via the Cochrane Collaboration’s tool and the Newcastle-Ottawa Scale (NOS). Data will be collected via standard form. Reviewers disagreement will be solved throughout debate, or by referring to a third opinion. This protocol will adhere the recommendations appointed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).DiscussionThe outcomes of this systematic review are highly significant for dental education exploring the importance of enhancing haptic simulation during preclinic prosthetic training.Recognizing the strengths and weaknesses of virtual reality (VR) in relation to traditional preclinical training methods is crucial for developing effective educational strategies. This understanding can lead to enhanced training outcomes and increased student satisfaction, which in turn contributes to improved quality of clinical prosthetic services.Systematic review registrationPROSPERO: CRD42024603681 (Registered on 30/10/2024).
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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 | 0.103 | 0.231 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.019 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.094 | 0.009 |
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".