Methods to Estimate V˙O2max upon Acute Hypoxia Exposure
Bibliographic record
Abstract
INTRODUCTION: Altitude and an individual's V˙O2max contribute to a decrease in V˙O2max under hypoxic conditions. The purpose of this study was to update previous reviews with recent research in order to quantitatively determine the individual and interacting effects of altitude and baseline V˙O2max on V˙O2max upon acute exposure to hypoxia while developing a statistical model to predict an individual's V˙O2max under hypoxic conditions. METHODS: Meta-regression was conducted on 105 independent groups of participants (n = 958 subjects from 80 different studies). A series of regression models was tested. The final model included altitude, baseline V˙O2max, Alt2, baseline V˙O2max2, and the interaction of altitude with baseline V˙O2max. RESULTS: A curvilinear model provided the best fit for metadata, explaining almost 80% of the variance in the null model. Nonlinear effects of Alt2 (β = -0.078; 95% confidence interval, -0.15 to -0.002) and baseline V˙O2max2 (β = -0.003; 95% confidence interval, -0.004 to -0.001) showed that V˙O2max decreases as altitude increases and that the decrease is greater in individuals with higher aerobic capacities. The interaction of these effects (β = -0.028; 95% confidence interval, -0.042 to -0.015) also showed that the effects of altitude were augmented with higher baseline aerobic capacities. Furthermore, the predictions of the model were fairly accurate in predicting individual decreases in V˙O2max (root-mean-squared error, 3.9 mL·kg(-1)·min(-1)). CONCLUSIONS: These data provide a robust quantitative framework for the curvilinear and interacting effects of altitude and baseline V˙O2max in determining an individual's effective V˙O2max at altitude. This predictive model is useful for a priori power calculations, design of future experimental studies, and prediction of aerobic capacity declines in applied settings.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".