A new aerobic fitness score based on lactate sensing during submaximal exercise
Bibliographic record
Abstract
Given the known clinical utility of cardiorespiratory fitness, measurement of physiological responses to submaximal exercise may be a feasible approach well suited for diagnostic and prognostic purposes in non-athletes and the chronically ill. Lactate levels and watt output during a short submaximal exercise and a subsequent relaxation period yield an aerobic score that is consistent with cardiorespiratory fitness grades in non-athletes and that may be used as a marker in such approaches. In this study, 28 females (23 ± 3 years) were submitted to a 15 min submaximal recumbent bike session, and their capillary and saliva lactate concentrations were recorded and plotted against time. An individual aerobic score was calculated from this curve, using watt output during equal relative percentiles of maximum heart-rate benchmarks. The scores were compared with respective results in a V̇O2max test and with a similar scoring system in 14 older (51 ± 9 years) females; they correlated with the 6 categories of the V̇O2max test results and classified into 3 categories of V̇O2max grades (very poor/poor; fair/good; excellent/superior) with a combined accuracy of 80.95%. More studies are required to validate the potential utility of this submaximal test as an additional risk factor for diagnostic purposes in non-athletes. Novelty points: A novel method for estimating cardiorespiratory fitness during submaximal exercise consistent with V̇O2max performance. The method yields an aerobic score that may be used as a marker for diagnostic and prognostic purposes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".