Out-of-hospital cardiac arrest in countries of the Gulf Cooperation Council: a scoping review
Bibliographic record
Abstract
BACKGROUND: Published data are lacking on response to and outcomes of out-of-hospital cardiac arrest in the Middle East. What data there are have not been comprehensively analysed. AIMS: This study aimed to assess the characteristics of people with out-of-hospital cardiac arrest in Gulf Cooperation Council (GCC) countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates) and the response to and outcomes of such incidents. METHODS: This was a scoping review of published and grey literature on out-of-hospital cardiac arrest in GCC countries from 1990 to June 2019. Studies in English and Arabic were eligible for inclusion. MEDLINE, CINAHL, Web of Science and EMBASE were searched as well as relevant non-indexed journals. Google searches were also done. References of included studies were scanned for relevant articles. Experts on the subject in the region were consulted. RESULTS: Of 647 citations retrieved, 24 studies were included for data extraction and analysis. No literature was identified for Bahrain. People with out-of-hospital cardiac arrest in the region were younger, predominantly male and had more comorbidity than reported in other regions of the world. Use of emergency medical services was low across the GCC countries, as was bystander cardiopulmonary resuscitation, return of spontaneous circulation and survival to discharge. CONCLUSIONS: A coordinated effort to address out-of-hospital cardiac arrest, including the generation of research, is lacking within and among GCC countries. Establishment of lead agencies responsible for developing and coordinating strategies to address out-of-hospital cardiac arrest, such as community response, public education and reporting databases, is recommended.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 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".