Do we underestimate maximal oxygen uptake in cancer survivors? Findings from a supramaximal verification test
Bibliographic record
Abstract
Cancer survivors demonstrate a reduced maximal oxygen uptake, which is clinically relevant in terms of overall survival. However, it remains uncertain whether they attain their “true maximal oxygen uptake” in a cardiopulmonary exercise test (CPET). In the present study, a supramaximal verification bout (Verif) was applied in cancer survivors to confirm attainment of maximal oxygen uptake. Seventy-five participants (age, 61 ± 12 years; n = 43 females with breast cancer and n = 32 males with prostate cancer, 6–52 weeks after primary therapy) performed a CPET on a cycle ergometer and a Verif at 110% peak power output. As verification criterion, maximal oxygen uptake in Verif should not exceed maximal oxygen uptake in CPET by >3%. On average, maximal oxygen uptake was significantly lower in Verif compared with CPET (1.60 ± 0.38 L·min–1 vs. 1.65 ± 0.36 L·min–1, p = .023). On the individual level, n = 51 (68%) satisfied the verification criterion, whereas n = 24 (32%) demonstrated a higher maximal oxygen uptake in Verif. n = 69 (92%) fulfilled ≥2 secondary criteria for maximal exhaustion in the CPET. While maximal oxygen uptake was not underestimated in the CPET on average, one-third of cancer survivors did not attain their true maximal oxygen uptake. Verif appears feasible and beneficial to confirm true maximal oxygen uptake in this population. Furthermore, it might be more reliable than secondary criteria for maximal exhaustion. Novelty In about one-third of cancer survivors, maximal oxygen uptake is underestimated by a CPET. This underestimation of maximal oxygen uptake is not necessarily indicated by secondary criteria for maximal exhaustion. A supramaximal verification bout appears feasible and helpful for the determination of maximal oxygen uptake in cancer survivors.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".