Knowledge and Practice of General Dental Practitioners Concerning Dental Trauma Management in Children in Ahvaz, Iran
Bibliographic record
Abstract
Background and Objectives: Traumatic dental injuries (TDIs) are unpleasant experiences for children and they necessitate to be treated as soon as possible. This cross-sectional study aimed to assess the knowledge and practice of general dental practitioners (GDPs) regarding emergency management of TDIs in Ahvaz, Iran.Subjects and Methods: In this study, a two-part questionnaire was responded by 100 GDPs. The first section included questions on demographic information and the second section was composed of questions on different dental Injuries. One score was assigned to each correct answer; the total score of 10 to 30 was considered as low knowledge and practice, while scores 30-50, 50-70 and above 70 were considered as moderate, good, and high levels of knowledge and practice, respectively. The data were analyzed using Pearson’s Correlation, t-test and regression.Results: With regards to the level of GDP’s knowledge, the mean score was 59.2%. A total of 100 (51%) dentists showed a good level of knowledge. A significant association was found between knowledge and practice of GDPs in their practice encountering and treating TDI (P=0.001).Conclusion: The overall knowledge of GDPs about management of TDI in the selected community was good.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".