Adjustment for gas exchange threshold enhances precision of heart rate-derived <i>V</i>O<sub>2</sub> estimates during heavy exercise
Bibliographic record
Abstract
Overestimates of oxygen uptake (VO2) are derived from the heart rate reserve-VO2 reserve (HRR-VO2R) model. We tested the hypothesis that adjusting for differences above and below gas exchange threshold (HRR-GET model) would tighten the precision of HR-derived VO2 estimates during heavy exercise. Seven men and 7 women of various VO2 max levels, on 2 separate days, cycled for 6 min at intensities equal to power at GET, 15% the difference between GET and VO2 max (15% above), and at 30% above GET. A second bout at 15% above GET (15% above (bout 2)) after 3 min of recovery was performed to assess estimates during interval training. Actual VO2 was compared with estimates derived from the HRR-VO2R and the HRR-GET. VO2 values were summed over the 6 min duration of data collection (6 min LO2) and compared with Bland-Altman plots. HRR-VO2R yielded 6 min LO2 (+/-2 SD) overestimates of 2.0 (+/-2.5), 1.9 (+/-2.7), and 1.3 (+/-3.3) for GET, 15% over, and 30% over, respectively, whereas corresponding 6 min LO2 difference values for the HRR-GET model were -0.42 (+/-1.6), -0.23 (+/-1.1), and -0.55 (+/-1.8), respectively. For 15% above (bout 2), the 6 min LO2 difference for HRR-VO2R was 1.8 (+/-2.9), whereas the difference for HRR-GET was 0.17 (+/-1.4). The 6 min LO2 values relative to the subjects' VO2 max did not vary (r=0.05 to 0.36); therefore, fitness level did not affect estimates. Sex did not affect accuracy of either estimate model (sex X estimate model interaction, p>0.95). We observed accurate estimates from the HRR-GET model during heavy exercise.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".