Knowledge and Practice Regarding Oral Cancer: A Study Among Dentists in Jakarta, Indonesia
Bibliographic record
Abstract
OBJECTIVE: To assess Indonesian dentists' knowledge of risk factors and diagnostic procedures related to oral cancer (OC) and to determine the factors that influenced their level of knowledge. METHODS: A modified version of a questionnaire that had been used to assess dentists' knowledge regarding OC in Canada was used. A total of 816 dentists were invited to participate in the study. RESULTS: The total response rate was 49.2%; however, the number of dentists from 5 regions in Jakarta were equally represented. Use of tobacco or alcohol and history of previous OC were the top 3 risk factors that were answered correctly by dentists, but there was a high proportion of dentists who considered some without any evidence as risk factors. Almost half of the dentists did not know the early signs of OC and that erythroplakia and leukoplakia were associated with increased risks of developing OC. Only about 27% of dentists had a high level of knowledge of risk factors and fewer dentists demonstrated a good knowledge of diagnostic procedures. Dentists' age group, year of graduation, and experience of continuing education significantly influenced the level of knowledge of diagnostic procedures (P < .05). CONCLUSION: Dentists in Jakarta had a considerable level of knowledge of major risk factors of OC, although some gaps in their knowledge, especially in diagnostic procedures, were present. Increasing these competencies may aid in the prevention and early detection of OC.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".