High Altitude Illness among Rapidly Ascending Pilgrims to Kailash Mansarovar
Bibliographic record
Abstract
BACKGROUND: High altitude pilgrims typically ascend rapidly, are not well prepared for the austere environment and tend to have multiple co-morbidities. Here, we list the trend of altitude and other illnesses who visited Humla district hospital (2,950 meters) following very rapid ascent to Kailash Mansarovar (4,500 meters). METHODS: A prospective study was conducted among 55 patients at the Humla District Hospital from September 2019 - August 2022. Patients who fell ill during pilgrimage and brought to the hospital were included. The patients were assessed with medical history and clinical examination. Lake Louise Score Acute Mountain Sickness Score (2018) was used for the diagnosis of Acute Mountain Sickness. RESULTS: A total of 56 evacuees visited the hospital which included 55 patients and 1 brought dead. The mean age was 50.63 ± 10.91 years. Sixteen patients (29.1%) developed symptoms within 24 hours and 15 patients (27.3%) within 48 hours of ascent. Headache 42 (76.4%) was the most common complain. Mild acute mountain sickness (30.9%; 17 patients) was the most common altitude related illness while 14 patients (25.4%) were diagnosed with non-altitude related illnesses. Twelve patients (21.8%) had co-morbidities like hypertension and diabetes mellitus. CONCLUSIONS: In the rapidly ascending pilgrims, majority of travelers requiring medical attention are suffering from some form of altitude illnesses. Hence, proper planning and public awareness about slow and gradual ascent profile is necessary to make the travel safer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".