Risk Determinants of Acute Mountain Sickness in Trekkers in the Nepali Himalaya: A 36-Year Follow-Up
Bibliographic record
Abstract
Cameron, Hannah, Marion McDevitt, Bengt Kayser, Craig Kutz, Suvash Dawadi, and Alana Hawley. Risk determinants of acute mountain sickness in trekkers in the Nepali Himalaya: a 36-year follow-up. High Alt Med Biol. 27:17-22, 2026. INTRODUCTION: Non-acclimatized trekkers risk developing acute mountain sickness (AMS) at high altitudes. We surveyed trekkers on the Annapurna Circuit in Nepal (peak 5,416 m) to assess AMS incidence and risk factors. Results were compared to 1986, 1998, and 2010 surveys. METHODS: Paper and electronic surveys were distributed to English-speaking trekkers who stopped at the Manang Aid Post (3,500 m). AMS was assessed with the Lake Louise Score (LLS; cutoffs ≥3 and ≥5) and the Environmental Symptom Questionnaire AMS-C score (cutoff ≥0.7). RESULTS: One hundred and forty-three surveys were returned. Incidence of AMS was 45%, 29%, and 19% (LLS ≥3, LLS ≥5, and AMS-C). AMS incidence was similar to that in 2010 and lower than in 1986 and 1998. In this study, body mass index (BMI) was a significant risk factor for AMS. Seventy-five percent of trekkers had elementary awareness of AMS, compared to 42% in 2010. Trekkers had slower ascent rates and 49% used prophylactic acetazolamide, compared to 44% (2010), 12% (1998), and 1% (1986). CONCLUSIONS: BMI was a predictor of AMS. Awareness of AMS was greater when compared to past studies; however, AMS rates stayed relatively stable between 2010 and the present. Whether awareness reduces the incidence of other potentially lethal altitude illnesses requires further investigation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".