The Pattern of Referral of Sick Omani Pilgrims From the Omani Medical Mission During Hajj 2019
Bibliographic record
Abstract
Background Annually, in the month of Dhul hijjah, over 2 million Muslims travel to Saudi Arabia to perform hajj. Hajj is the biggest mass gathering globally, which creates a substantial influence on hajjes’ health. The Omani medical mission is the official delegation from the Omani government to Saudi Arabia to serve the Omani hajjees regarding their health issues. Objective This study investigates the referral rate and pattern of diseases among hajjees referred by the Omani medical mission during Hajj 1440 H. Methods We conducted a cross-sectional study at the Omani Medical missions in Makkah, Madinah, Mina, and Arafat. Data was collected via a predesigned form. All Omani pilgrims presenting to the mission who were referred to local hospitals were included. Results The total number of cases was 5000, of which 106 (2.1%) were referred to local hospitals (21.2 per 1000 hajjees). The most common causes of referral were cardiovascular diseases (23.6%), followed by gastrointestinal disease (17.9%) and trauma (16.9%). Male patients comprised 60.1% of the sample. Their mean age was 47.3 (SD 11.27) years, with the highest referrals in the 51-60 years age group (30%). Over half (55.7%) had comorbidities. Patients’ mean time to reach the clinic was 8.87 (SD 6.41) minutes, with 65% arriving in 5 minutes or less. The mean time needed to reach the hospital by ambulance was 11.39 (SD 6.6) minutes, with 36% arriving within 5 minutes. Of the referrals, 42% were admitted into the hospital. Hospitalization was significantly higher among patients with chest pain (P<.006), diabetics (P<.001), and patients with heart disease (P=.01). Conclusions The most common causes for referral of hajjees from the Omani Medical Mission were cardiovascular diseases, gastrointestinal disease, and trauma. This information should assist the Omani government in planning their medical services in the hajj season in future years.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".