Evidence‐based dentistry for planning restorative treatments: barriers and potential solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Evidence-based dentistry (EBD) can help provide the best treatment option for every patient, however, its implementation in restorative dentistry is very limited. OBJECTIVE: This study aimed at assessing the barriers preventing the implementation of EBD among dental undergraduate and graduate students in Montreal, and explore possible solutions to overcome these barriers. MATERIALS AND METHODS: A cross-sectional survey was conducted by means of a paper format self-administrated questionnaire distributed among dental students. The survey assessed the barriers and potential solutions for implementation of an evidence-based practice. RESULTS: Sixty-one students completed the questionnaire. Forty-one percent of respondents found evidence-based literature to be the most reliable source of information for restorative treatment planning, however, only 16% used it. They considered that finding reliable information was difficult and they sometimes encountered conflicting information when consulting different sources. Dental students had positive attitudes towards the need for better access to evidence-based literature to assist learning and decision making in restorative treatment planning and to improve treatment outcomes. Even for dentists trained in EBD, online searching takes too much time, and even though it can provide information of better quality than personal intuition, it might not be enough to identify the best available evidence. CONCLUSIONS: Even though dental students are aware of the importance of EBD in restorative dentistry they rarely apply the concept, mainly due to time constraints. For this reason, implementation of EBD would probably require faster access to evidence-based knowledge.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it