Knowledge, Skills, and Perceived Competency in Handling Medical Emergencies among Dental House Officers and General Dentists
Bibliographic record
Abstract
Dental practitioners frequently encounter medical emergencies due to the nature of their work and the inherent stress within a dental office. Adequate preparation and confidence in handling such emergencies are very important for patient safety. Objectives: To assess the knowledge, skills and perceived competency of house officers and general dentists in managing medical emergencies in dental practice. Methods: A cross-sectional survey was conducted in Lahore among house officers and general practitioners. The survey included a pre-valid questionnaire on medical history documentation, attendance at medical emergency workshops, confidence in performing cardiopulmonary resuscitation, administering intravenous drugs, and managing common emergencies like syncope and hypoglycemia. Statistical analysis was performed with Chi-square and Fisher’s exact tests applied to assess associations. Results: The majority of participants demonstrated adequate knowledge of medical emergency protocols, with 68% aware of the need to record medical history and 73% familiar with universal precautions. However, 45% reported being trained in administering cardiopulmonary resuscitation and 29% in administering intravenous drugs. Confidence in handling emergencies such as syncope (0.004) and unconscious hypoglycemic patients (p-=0.03) was significantly higher among dentists with more experience. Conclusions: It was concluded that while dental practitioners generally possess knowledge about medical emergency protocols, there is a gap in training and confidence particularly in administering lifesaving procedures. More experienced dentists demonstrated higher confidence compared to house officers.
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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 | 0.005 |
| 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) | 0.003 | 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".