Knowledge about traumatic dental injuries in the permanent dentition: A survey of Lithuanian dentists
Bibliographic record
Abstract
BACKGROUND/AIMS: In Lithuania, dental trauma cases are often treated by general dentists, but it is unknown whether their age, self-evaluation of trauma knowledge and practice location can predict their actual knowledge and management of trauma cases. The aim of this study was to evaluate whether these factors can be used to predict the actual knowledge and management of trauma cases. METHODS: A 2-part questionnaire included 17 multiple-choice questions about practitioners' demographics, their self-evaluated knowledge and how frequently they treated traumatized permanent teeth as well as 13 clinical scenarios reflecting a variety of clinical trauma cases and their complications. A total of 980 randomly selected general dentists, representing 5 Lithuanian counties, participated in the study. RESULTS: The response rate was 59.4% (n = 582). Overall, 82.3% of general dentists reported that they treated only a few dental trauma cases and 14.4% chose to refer their patients; 55.1% of dentists considered their dental traumatology knowledge to be sufficient but incomplete and 34.0% self-evaluated their knowledge as insufficient. The most knowledge (based on self-evaluation) was reported by the younger dentists (≤50 years; P = .004). The mean correct knowledge score was 7.6 ± 2.2 of the 13 clinical scenarios. Both bivariate and multivariate analyses showed that greater trauma-related knowledge was associated with a younger age. Better knowledge was observed amongst the dentists who self-evaluated their own knowledge as sufficient or as comprehensive. CONCLUSION: Lithuanian general dentists have insufficient dental traumatology knowledge. Better knowledge was observed amongst younger dentists (≤50 years).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".