Similar maximal oxygen uptake assessment from a step cycling incremental test and verification tests on the same or different day
Bibliographic record
Abstract
The present study aimed to compare maximal oxygen uptake of a step incremental test with time to exhaustion verification tests (TLIM) performed on the same or different day. Nineteen recreationally trained cyclists (age: 23 ± 2.7 years; maximal oxygen uptake: 48.0 ± 5.8 mL·kg−1·min−1) performed 3 maximal tests as follows: (i) same day: an incremental test with 3-min stages followed by a TLIM at 100% of peak power output of the incremental test (TLIM-SAME) interspaced by 15 min; and (ii) different day: a TLIM at 100% of peak power output of the incremental test (TLIM-DIFF). The maximal oxygen uptake was determined for the 3 tests. The maximal oxygen uptake was not different among the tests (incremental: 3.83 ± 0.41; TLIM-SAME: 3.72 ± 0.42; TLIM-DIFF: 3.75 ± 0.41 L·min−1; P = 0.951). Seven subjects presented a variability greater than ±3% in both verification tests compared with the incremental test. The same-day verification test decreased the exercise tolerance (240 ± 38 vs. 310 ± 36 s) compared with TLIM-DIFF (P < 0.05). In conclusion, the incremental protocol is capable of measuring maximal oxygen uptake because similar values were observed in comparison with verification tests. Although the need for the verification phase is questionable, the additional tests are useful to evaluate individual variability. Novelty Step incremental test is capable of measuring maximal oxygen uptake with similar values during TLIM on the same or different day. Although the necessity of the verification phase is questionable, it can allow the determination of variability in maximal oxygen uptake.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".