Prophylactic Acetaminophen or Ibuprofen Result in Equivalent Acute Mountain Sickness Incidence at High Altitude: A Prospective Randomized Trial
Bibliographic record
Abstract
OBJECTIVE: Recent trials have demonstrated the usefulness of ibuprofen in the prevention of acute mountain sickness (AMS), yet the proposed anti-inflammatory mechanism remains unconfirmed. Acetaminophen and ibuprofen were tested for AMS prevention. We hypothesized that a greater clinical effect would be seen from ibuprofen due to its anti-inflammatory effects compared with acetaminophen's mechanism of possible symptom reduction by predominantly mediating nociception in the brain. METHODS: A double-blind, randomized trial was conducted testing acetaminophen vs ibuprofen for the prevention of AMS. A total of 332 non-Nepali participants were recruited at Pheriche (4371 m) and Dingboche (4410 m) on the Everest Base Camp trek. The participants were randomized to either acetaminophen 1000 mg or ibuprofen 600 mg 3 times a day until they reached Lobuche (4940 m), where they were reassessed. The primary outcome was AMS incidence measured by the Lake Louise Questionnaire score. RESULTS: Data from 225 participants who met inclusion criteria were analyzed. Twenty-five participants (22.1%) in the acetaminophen group and 18 (16.1%) in the ibuprofen group developed AMS (P = .235). The combined AMS incidence was 19.1% (43 participants), 14 percentage points lower than the expected AMS incidence of untreated trekkers in prior studies at this location, suggesting that both interventions reduced the incidence of AMS. CONCLUSIONS: We found little evidence of any difference between acetaminophen and ibuprofen groups in AMS incidence. This suggests that AMS prevention may be multifactorial, affected by anti-inflammatory inhibition of the arachidonic-acid pathway as well as other analgesic mechanisms that mediate nociception. Additional study is needed.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".