Is previous history a reliable predictor for acute mountain sickness susceptibility? A meta-analysis of diagnostic accuracy
Bibliographic record
Abstract
PURPOSE: The goal of this meta-analysis was to determine the clinical utility of acute mountain sickness (AMS) history to predict future incidents of AMS. METHOD: 17 studies (n=7921 participants) were included following a systematic review of the literature. A bivariate random-effects model was used to calculate the summary sensitivity and specificity of the diagnostic test, and moderator variables were tested to explain the heterogeneity across studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) method was used to assess concerns for bias and applicability for the included studies. RESULTS: The history of AMS had a low diagnostic accuracy for the prediction of future AMS incidents: the summary sensitivity was 0.50 (95% CI (0.40 to 0.59)) and the summary specificity was 0.72 (95% CI (0.66 to 0.78)). There was significant heterogeneity in the sensitivity and specificity across studies, which we modelled using moderator analysis. Studies that restricted the use of acetazolamide and dexamethasone had not only a higher sensitivity (0.66) relative to those that did not (0.44; p=0.03) but also an increased false-positive rate (0.39 vs 0.23, p=0.03). The QUADAS-2 analysis showed that AMS histories were insufficiently detailed, and few studies controlled for prophylactic medication use or recent altitude exposure, leading to high risks of bias and concerns for applicability. CONCLUSIONS: The use of AMS history to guide prophylactic strategies for high-altitude ascent is not supported by the literature; however, the low sensitivity and specificity of this diagnostic test could reflect the quality of the available studies. Ensuring that the characteristics of the history and future ascents are similar may improve the clinical utility of AMS history.
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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 | 0.026 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.057 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".