Impact of data averaging strategies on VO<sub>2max</sub> assessment: Mathematical modeling and reliability
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Bibliographic record
Abstract
Background No consensus exists on how to average data to optimize O 2max assessment. Although the O 2max value is reduced with larger averaging blocks, no mathematical procedure is available to account for the effect of the length of the averaging block on O 2max. Aims To determine the effect that the number of breaths or seconds included in the averaging block has on the O 2max value and its reproducibility and to develop correction equations to standardize O 2max values obtained with different averaging strategies. Methods Eighty‐four subjects performed duplicate incremental tests to exhaustion (IE) in the cycle ergometer and/or treadmill using two metabolic carts (Vyntus and Vmax N29). Rolling breath averages and fixed time averages were calculated from breath‐by‐breath data from 6 to 60 breaths or seconds. Results O 2max decayed from 6 to 60 breath averages by 10% in low fit ( O 2max < 40 mL kg −1 min −1 ) and 6.7% in trained subjects. The O 2max averaged from a similar number of breaths or seconds was highly concordant (CCC > 0.97). There was a linear‐log relationship between the number of breaths or seconds in the averaging block and O 2max ( R 2 > 0.99, P < 0.001), and specific equations were developed to standardize O 2max values to a fixed number of breaths or seconds. Reproducibility was higher in trained than low‐fit subjects and not influenced by the averaging strategy, exercise mode, maximal respiratory rate, or IE protocol. Conclusions The O 2max decreases following a linear‐log function with the number of breaths or seconds included in the averaging block and can be corrected with specific equations as those developed here.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it