Does hysteresis in cardiorespiratory variables during high altitude ascent and descent quantify the magnitude of acclimatization
Bibliographic record
Abstract
Acute mountain sickness (AMS) is common during ascent to high altitude. We aimed to quantify ventilatory acclimatization in the context of high altitude hypoxia by comparing oxygen saturation (SpO2) and end-tidal CO2 (PETCO2) during ascent and descent. We hypothesized (1) the hysteresis area formed by differential cardiorespiratory values during ascent and descent would quantify the magnitude of acclimatization, (2) prolonged stays above 5000m would lead to larger areas than shorter stays, and (3) larger areas would be correlated with lower AMS symptoms. In 2012, a single male participant (P1) ascended to 5050m in Nepal and stayed approximately three weeks. P1 returned in 2016 as part of a group of 20 participants (P20), and stayed only one night above 5000m. In P1, the calculated SpO2 hysteresis area was 72% larger in 2012 than 2016. In P20, there was a weak positive correlation between SpO2 and PETCO2 hysteresis areas (r=0.43, P=0.06), but these values were not correlated with AMS severity when arriving above 5000m (r=0.06, P=0.8; r=0.3, P=0.2, respectively). We conclude that calculation of hysteresis in cardiorespiratory variables may hold promise as a simple method to quantify the magnitude of ventilatory acclimatization when staying at altitude for prolonged periods. * Indicates faculty mentor.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".