Reproducibility of peak oxygen consumption and the impact of test variability on classification of individual training responses in young recreationally active adults
Bibliographic record
Abstract
Summary This study investigated whether VO2peak is reproducible across repeated tests before (PRE) and after (POST) training, and whether variability across tests impacts how individual responses are classified following 3 weeks of aerobic exercise training (cycle ergometry). Data from 45 young healthy adults (age: 20·1 ± 0·9 years; VO2peak, 42·0 ± 6·7 ml·min−1) from two previously published studies were utilized in the current analysis. Non‐responders were classified as individuals who failed to demonstrate an increase or decrease in VO2peak that was greater than 2·0 times the typical error of measurement (107 ml·min−1) away from zero, while responders and adverse responders were above and below this cut‐off, respectively. VO2peak tests at PRE (three total) and POST (three total) were highly reproducible (PRE and POST average and single measures ICCs: range 0·938–0·992), with low coefficients of variation (PRE:4·9 ± 3·1%, POST: 4·8 ± 2·7%). However, a potential learning effect was observed in the VO2peak tests prior to training, as the initial pretraining test was significantly lower than the third (p = 0·010, PRE 1: 2 946 ± 924 ml·min−1, PRE 3: 3 042 ± 919 ml·min−1). This resulted in fewer individuals classified as adverse responders for Test 3 compared to any combination of tests that included Test 1, suggesting that a single ramp test at baseline may not be sufficient to accurately classify the VO2peak response in young recreationally active individuals. Thus, it is our recommendation that the initial VO2peak test be used as a familiarization visit and not included for analysis.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".