A Cross-Sectional Study of Drowning in Saudi Arabia Using First Responder Data
Bibliographic record
Abstract
Drowning is a significant public health issue and is considered one of the most avoidable yet underappreciated causes of mortality worldwide. Drowning likely presents a significant public health burden in Saudi Arabia, especially in children. To our knowledge only three studies have been conducted investigating drowning in Saudi Arabia, all of which were single center hospital-based studies. This is the first national cross-sectional study to assess drowning epidemiology in Saudi Arabia and the first to utilize first responder data. This study's main objectives are to quantify the burden of drowning in Saudi Arabia, and describe the epidemiological profile and geographic distribution of drowning cases across the country. We conducted a cross-sectional study using electronic data from the Saudi Red Crescent Authority (SRCA) on drowning incidents between January 2019 to November 2021. Descriptive statistics such as frequencies and percentages were calculated using the Statistical Package for the Social Sciences (SPSS), software for Windows (version 23.0). Men and children under 4 had the highest incidence of drowning, with 2021 incidence rates of 19.86 and 54.08 per million, respectively. The regions of Makkah, Riyadh, and Dammam, had the largest number of cases, accounting for 30.49% (n = 501), 19.84% (n = 326), and 13.02% (n = 214) of all cases respectively. The regions with the highest drowning incidence in 2021 were Tabuk, Al-baha, Makkah, Jizan, and Hail with incidence rates of 32.32, 27. 96, 22.28, 20.97, and 20.5 per million people, respectively. Drowning is a leading cause of death in Saudi Arabia and globally, with young children most at risk. Our study identifies data gaps and high-incidence areas requiring further investigation. The low bystander cardiopulmonary resuscitation (CPR) rate and high mortality call for better public education on drowning prevention and first aid.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".