Including supramaximal verification reduced uncertainty in VO<sub>2peak</sub> response rate
Bibliographic record
Abstract
Many reports describe using a supramaximal verification phase—exercising at a power output higher than the highest power output recorded during an incremental cardiopulmonary test—to validate VO2max. The impact of verification phases on estimating the proportion of individuals who increased VO2peak in response to high-intensity interval training (HIIT) remains an underexplored area in the individual response literature. This analysis investigated the influence of same-day and separate-day verification phases during repeated measurements (incremental tests—INCR1 and INCR2; incremental tests + supramaximal verification phases—INCR1+ and INCR2+) of VO2peak on typical error (TE) and the proportion of individuals classified as responders (i.e., the response rate) following 4 weeks of HIIT ( n = 25) or a no-exercise control period ( n = 9). Incorporation of supramaximal verification consistently reduced the standard deviation of individual response, TE, and confidence interval (CI) widths. However, variances were statistically similar across all groups ( p > 0.05). Response rates increased when incorporating either one (INCR1 to INCR1+; 24%–48%, p = 0.07) or two (INCR2 to INCR2+; 28%–48%, p = 0.063) supramaximal verification phases. However, response rates remained unchanged when either zero-based thresholds or smallest worthwhile difference response thresholds were used (50% and 90% CIs, all p > 0.05). Supramaximal verification phases reduced random variability in VO2peak response to HIIT. Compared with separate-day testing (INCR2 and INCR2+), the incorporation of a same-day verification (INCR1+) reduced CI widths the most. Researchers should consider using a same-day verification phase to reduce uncertainty and better estimate VO2peak response rate to HIIT.
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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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".