Comparison of the knowledge and comfort zone of the manitoban general and pediatric dentists in treating dental trauma
Bibliographic record
Abstract
Injury to the teeth in pediatric patients may have serious and far-reaching consequences, including a significant emotional impact. The objective of this study was to compare and evaluate the knowledge of Manitoba general and pediatric dentists in treating dental trauma patients. A web-based survey was sent to all 19 Manitoba pediatric dentists, and a random 25% sample size of Manitoba general dentists (145 general dentists). Statistical analysis was performed using R program. The non-parametric Wilcoxon Mann-Whitney test was used to compare the two groups. For all of the analyses, the significance level of <0.05 was chosen to show strong evidence against the null hypothesis. The main finding was that the majority of pediatric dentists treated trauma patients with more severe injuries, such as luxation, avulsion and alveolar fracture, much more frequently than general dentists. Additionally, a large number of dentists did not feel very confident about their knowledge and skills in treating trauma patients (P< 0.05). Overall, multiple statistically significant differences between the two groups were alarming, and changes in the teaching curriculum may help to improve treatment outcomes in children with dental trauma.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".