The epidemiology of Hajj-related critical illness
Bibliographic record
Abstract
RATIONAL: The annual Hajj experience has direct relevance for other jurisdictions planning rapid deployment strategies for intensive care for large groups during expected or emergent events. OBJECTIVE: Approximately 2-3 million Muslims from over 160 countries travel to Saudi Arabia each year for Hajj. These pilgrims are typically older adults with a spectrum of comorbid conditions and of various ethnicities. This, coupled with a 2-wk period of physical migration in close contact with others, can lead to acute and critical illness from a variety of infectious and noninfectious causes and a requirement for full-scale but temporary intensive care to a large population. We describe patient characteristics, patterns of disease, and critical illness, including episodes of Influenza A 2009 (H1N1), therapies delivered, and clinical outcomes. METHODS: Prospective cohort study of 110 critically ill patients in four hospitals during the 2009 ("1431": November 18 to December 4) Hajj in Saudi Arabia. MEASUREMENTS AND MAIN RESULTS: Median (interquartile range) age was 60.5 (51.3-70) yrs, 69 (62.7%) were male, and Acute Physiology and Chronic Health Evaluation IV score was 60.5 (47-78.3). Forty-one patients (37.3%) were critically ill due to cardiovascular diseases (23.6% with myocardial infarction); 51 (46.4%) had severe infections (21.8% with H1N1); electrolyte disturbance (21.8%); or pulmonary illness (15.5%). Sixty patients (54.6%) required ventilation. Median predicted mortality by Acute Physiology and Chronic Health Evaluation IV was 14% while actual short-term mortality was 6.4% (p = .009). Longer-term mortality may be higher. CONCLUSION: Both event-specific conditions and patient-specific comorbid conditions are common causes of critical illness during large gatherings. With the ability to provide temporary but full-service intensive care, morbidity and mortality due to critical illness can be low, even among an older patient population and difficult care conditions.
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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 | 0.042 |
| 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 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".