Awareness and attitude towards using magnification loupes among dental students: a cross-sectional study
Bibliographic record
Abstract
Introduction: Precision in dental treatment can be achieved by perfect visualization, and magnification devices play an essential role.Magnification is considered one of the biggest revolutions in the realm of modern dentistry.Innovation is a cornerstone of dentistry, and the recent breakthrough boosted a number of global investigations and increased knowledge in this area.Objectives: The aim of this study was to evaluate the level of knowledge, awareness, and attitude towards using magnification loupes among dental students.Material and methods: A cross-sectional, questionnaire-based study was conducted in Jeddah City, Saudi Arabia.Study population was selected using cluster random sampling.Questionnaire was distributed online, and 255 dental students responded.Results: Data was tabulated and analyzed using statistical package for social sciences (SPSS) version 21.0.c2 test was applied to analyze data, and a p-value of less than 0.05 was considered statistically significant.Of the 255 respondents, 52% were females and 48% were males.50.8% of participants were between 24 and 26 years old.Conclusions: It was found that all respondents were aware of magnification loupes, and the most common sources of information were colleagues/friends, online/social networking sites, and company-sponsored demonstrations.Many individuals were aware of the advantages of using magnification loupes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".