Translating research evidence into dental practice
Bibliographic record
Abstract
Evidence-based dentistry relies on effectively translating research findings into dental practice to improve patient outcomes and population health. Translational research aims to bridge the gap between bench and bedside. During the past years, efforts have been made to facilitate the transfer of scientific findings into dental practice, to scale the provision of the best available evidence, and to incorporate it as part of the standard care. However, empirical evidence has shown considerable delays and unpredictability during this process. The main obstacles include variable and complex health conditions, diversity of practice settings, inefficient multidisciplinary collaboration, as well as avoidable research waste. This narrative review intends to introduce key concepts and principles for disseminating and implementing research findings, and discuss the challenges translational dentistry faces. Based on existing implementation strategies, we present knowledge and advances based on evidence-based medicine, including clinical practice guidelines, evidence ecosystem, implementation science, multidisciplinary collaboration, regulations, and standards, which hold promise for accelerating the translation and application of dental research evidence. • Translational research bridges the gap between bench and bedside to improve global healthcare. • Obstacles in dental translation include the lack of knowledge on translation principles and poor research quality • Approaches from an evidence-based perspective are recommended for improving dental research translation.
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 | 0.217 | 0.399 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".