Patient and System-Related Delays of Emergency Medical Services Use in Acute ST-Elevation Myocardial Infarction: Results from the Third Gulf Registry of Acute Coronary Events (Gulf RACE-3Ps)
Bibliographic record
Abstract
BACKGROUND: Little is known about Emergency Medical Services (EMS) use and pre-hospital triage of patients with acute ST-elevation myocardial infarction (STEMI) in Arabian Gulf countries. METHODS: Clinical arrival and acute care within 24 h of STEMI symptom onset were compared between patients transferred by EMS (Red Crescent and Inter-Hospital) and those transferred by non-EMS means. Data were retrieved from a prospective registry of 36 hospitals in 6 Arabian Gulf countries, from January 2014 to January 2015. RESULTS: We enrolled 2,928 patients; mean age, 52.7 (SD ±11.8) years; 90% men; and 61.7% non-Arabian Gulf citizens. Only 753 patients (25.7%) used EMS; which was mostly via Inter-Hospital EMS (22%) rather than direct transfer from the scene to the hospital by the Red Crescent (3.7%). Compared to the non-EMS group, the EMS group was more likely to arrive initially at a primary or secondary health care facility; thus, they had longer median symptom-onset-to-emergency department arrival times (218 vs. 158 min; p˂.001); they were more likely to receive primary percutaneous coronary interventions (62% vs. 40.5%, p = 0.02); they had shorter door-to-needle times (38 vs. 42 min; p = .04); and shorter door-to-balloon times (47 vs. 83 min; p˂.001). High EMS use was independently predicted mostly by primary/secondary school educational levels and low or moderate socioeconomic status. Low EMS use was predicted by a history of angina and history of percutaneous coronary intervention. The groups had similar in-hospital deaths and outcomes. CONCLUSION: Most acute STEMI patients in the Arabian Gulf region did not use EMS services. Improving Red Crescent infrastructure, establishing integrated STEMI networks, and launching educational public campaigns are top health care system priorities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".