Out of hospital cardiac arrests in the Gulf Region: a scoping review
Bibliographic record
Abstract
Abstract Background Out-of-hospital cardiac arrest (OHCA) is a major cause of mortality worldwide. Recent studies demonstrated low survival rates in Middle Eastern countries. Anecdotally there are unique demographic, cultural and logistical challenges in this region. However, there remains a paucity of data published on OHCA in the Middle East. In order to address OHCA in a meaningful manner in the region, we first need to quantify the issue. Methods We conducted a scoping review of published and grey literature on OHCA in the Gulf Cooperative Council region that utilised Arksey and O’Malley’s framework. Electronic databases and grey literature sources were identified and searched. Subject matter experts in the region were consulted. All types of studies in English and Arabic were included. Results A total of 24 studies were included from Saudi Arabia, the UAE, Oman, Kuwait, and Qatar. No literature was identified from the state of Bahrain. OHCA victims in the region are younger, predominantly male, and more co-morbid than other international studies. We observed low Emergency Medical Service utilisation, low bystander cardiopulmonary resuscitation, return of spontaneous circulation, and survival to discharge rates across the region. There are differences in characteristics of OHCA among ethnic groups. Discussion and conclusions We identified unique characteristics associated with OHCA in the region, variances in processes and outcomes, and a lack of coordinated effort to research and address OHCA. We recommend creating lead agencies responsible for coordinating and developing strategies such as community response, public education, and reporting databases.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".