Evaluating The Knowledge, Awareness and Exposure of Dentists in Conscious Sedation In Relation to Their Current Practice and Future Expectations
Bibliographic record
Abstract
Introduction: Conscious sedation in dentistry becoming more popular in Malaysia nowadays. However, the knowledge ,exposure and practice of conscious sedation in dental setting has rarely been explored. Therefore, this study aimed to evaluate the knowledge awareness and exposure of Malaysian dentists about conscious sedation for dentistry and their association with sociodemographic profile. Materials and Methods: A cross-sectional study using an online questionnaire (Qualtrics@ Software) was conducted among Malaysian registered dentists. Questions on sociodemographic profile, knowledge, awareness, exposure and practice on conscious sedation were collected. Chi square test was used to analyse the associated factors for knowledge, exposure and practice. Result: A total of 166 respondents completed the survey, resulting in response rate of 43.1%. Majority agreed that conscious sedation is beneficial in allaying dental anxiety and knows at least 3 types of CS. More than half had been exposed in inhalation sedation (IS) with less than half had CS exposure during undergraduate (UG). However, only a quarter practicing CS in their dental practice. Discussion: Malaysian dentists were familiar with the indications of CS in dentistry which corroborates with other studies. Meanwhile, the exposure to CS during undergraduate study were dependant on the availability and the program structure. Conclusion: Majority of respondents know, but only some are practicing oral and inhalation sedation. Only a few of respondents know about other types of CS. Thus, there is a need for more exposure and training of CS during undergraduate to cater the need of patients with dental anxiety.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".