Pulse oximetry for the prediction of acute mountain sickness: A systematic review
Bibliographic record
Abstract
Abstract Acute mountain sickness (AMS) causes serious illness for many individuals ascending to high altitude (HA), although preventable with appropriate acclimatisation. AMS is a clinical diagnosis, with symptom severity evaluated using the Lake Louise Score (LLS). Reliable methods of predicting which individuals will develop AMS have not been developed. This systematic review evaluates whether a predictive relationship exists between oxygen saturation and subsequent development of AMS. PubMed, PubMed Central, MEDLINE, Semantic Scholar, Cochrane Library, University of Birmingham Library and clinicaltrials.gov databases were systematically searched from inception to 15 June 2023. Human studies involving collection of peripheral blood oxygen saturation () from healthy lowlanders during ascent to HA that evaluated any relationship between and AMS severity were considered for eligibility. Risk of bias was assessed using a modified Newcastle–Ottawa Tool for cohort studies (PROPSPERO CRD42023423542). Seven of 980 total identified studies were ultimately included for data extraction. These studies evaluated and AMS (via LLS) in 1406 individuals during ascent to HA (3952–6300 m). Risk of bias was ‘low’ for six and ‘moderate’ for one of the included studies. Ascent profiles and measurement methodology varied widely, as did the statistical methods for AMS prediction. Decreasing oxygen saturation measured with pulse oximetry during ascent shows a positive predictive relationship for individuals who develop AMS. Studies have high heterogeneity in ascent profile and oximetry measurement protocols. Further studies with homogeneous methodology are required to enable statistical analysis for more definitive evaluation of AMS predictability by pulse oximetry.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".