Characteristics of Stroke in Prehospital Settings in Saudi Arabia: A Descriptive Analysis
Bibliographic record
Abstract
Background: Stroke is considered a time-sensitive emergency; thus, early recognition of this condition is a crucial function of emergency medical services (EMS) and medical practitioners. In this study, we aimed to assess the characteristics observed by EMS practitioners in stroke-suspected cases. Methodology: This is a retrospective observational study, using the data available in the registry of the Saudi Red Crescent Authority (SRCA). We collected data from the beginning of January 2018 to the end of December 2020. Results: We reviewed 753 patients who met the study’s inclusion criteria. Participants aged 61-70 years represented 29% of the study group, and 66% of the group were male. Patients living in Makkah constituted 32.9%, while most of the patients (71.7%) were Saudi nationals. Weakness was the most common complaint, reported in 45% of patients. The most associated disease was hypertension (54.4%), whereas hypoglycaemic patients represented 0.4% of the group. Conclusion: Weakness was the most prevalent complaint among stroke-suspected patients, and hypertension was the most associated risk factor. Blood glucose measurement and neurological examination were both included in the EMS assessment of stroke-suspected patients. This might indicate the high quality of the EMS assessment for stroke and stroke-mimickers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".