Knowledge and Decision-Making among Israeli Dentists Treating Young Patients with Type 1 Diabetes Mellitus: A Cross-Sectional Survey
Bibliographic record
Abstract
OBJECTIVE: To assess decision making process and knowledge level of dentists treating children with type 1 diabetes. STUDY DESIGN: Cross-sectional survey among dentistry residents and dental specialists working in clinics that provide dental care to children with type 1 diabetes. RESULTS: A total of 166 respondents were included. 42% of respondents perceived that they have sufficient knowledge to treat children with diabetes, in correlation with an average score of 1.9 out of 4 on knowledge questions. Over 80% of dentists decided to treat patients by consulting with the treating physician or by checking HbA1c and glucose blood levels independently. Greater knowledge was associated with a significantly higher tendency of the dentists to determine if the child's diabetes is controlled, and to refer less often to the hospital. Furthermore, greater knowledge was also associated with dentists' greater perception that they have enough knowledge, skills and confidence to treat children with diabetes. CONCLUSIONS: The study revealed significant gaps in the knowledge on diabetes among dentists who provide dental care to children. Dentists, pediatricians, endocrinologists, and other healthcare professionals who provide care for children should be encouraged to collaborate to create a mutual knowledgeable work environment for delivering best care to their patients.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".