Medical emergencies at teaching dental clinics in Saudi Arabia: a systematic review
Bibliographic record
Abstract
Medical emergencies (MEs) are sudden and severe health issues that pose an immediate threat to patients' lives or well-being if not addressed expeditiously. Annually, between 50 and 70% of dental professionals experience at least one medical emergency, with over a quarter encountering several emergencies each year. Therefore, it is essential to be familiar with the frequency and types of medical emergencies that may occur in dental offices so that dental professionals can be prepared to deal with life-threatening situations. This systematic review aims to report the prevalence and incidence of medical emergencies at teaching dental clinics in Saudi Arabia. Additionally, it aims to highlight assessments of practitioners' knowledge regarding these emergencies and report on the most encountered MEs. Furthermore, the study highlights recommendations from previous research on effective prevention and management strategies for MEs in dental teaching hospitals. The included articles were obtained from three different databases: EBSCO, SCOPUS, and PubMed. Eight studies were included in our analysis based on the eligibility criteria, and their quality assessment was assessed using the Newcastle-Ottawa Scale. The findings show a wide range of emergency reporting among the included articles, ranging from 49.4% to 0.2%. The dental students' knowledge in managing MEs was generally poor to below-average, except for one study that reported above-average performance. Postgraduate students showed better performance. Two studies reported a lack of confidence and practical competence in managing medical emergencies. This review demonstrates that although foundational awareness exists, readiness is often lacking, especially among students and junior practitioners. A standardized, competency-based training framework across Saudi dental institutions is imperative to bridge this gap and ensure patient safety. Further studies are recommended to evaluate the prevalence of medical emergencies and the level of knowledge across dental institutions in Saudi Arabia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".