Bibliographic record
Abstract
Dear Editor-in-Chief, The article “MRS evidence of adequate O2 supply in human skeletal muscle at the onset of exercise” (7) concluded that metabolic inertia—not O2 supply—is the major factor controlling the kinetics of V˙O2. This concept was supported by a delay in the increase in the deoxygenated myoglobin (deoxy-Mb) signal and similar rates of on-transient deoxy-Mb and phosphocreatine (PCr) kinetics; Richardson et al. (7) used a novel approach ([1H]magnetic resonance spectroscopy) to assess deoxy-Mb and thus O2 extraction. We were encouraged that the new technique for measuring “intramyocellular deoxygenation” responses during exercise on-transients confirmed previous measures of the time delay (TD) and time course profile of microvascular deoxygenation from our laboratory (1–3,5), using the simpler and more accessible tool of near-infrared spectroscopy. These comparative data were, however, not referenced. Nevertheless, in relation to the conclusions of the study, there are concepts that merit discussion. First, we believe that the interpretation might be affected by fitting strategies. For example, the PCr fit was constrained to go through the onset of exercise (fixed TD) to avoid the model fit projecting into pre-exercise time. Importantly, this TD is not physiological; by forcing the fit through the onset of exercise, the quality of this model would decrease and the time constant (τ) would be artificially reduced. Additionally, the deoxy-Mb signal is very noisy (small signal-to-noise ratio; Fig. 2); thus, these model parameter estimates are tenuous. Furthermore, we disagree with the use of the model-fit TD to determine the duration of deoxy-Mb TD; we believe that this TD should be calculated from the actual physiological increase in the signal, as presented for near-infrared spectroscopy–derived deoxygenated hemoglobin response (1–3). Finally, the myoglobin τ value should be calculated based on a fitting window beginning from the end of TD; as is, the model fit includes data points that do not belong to the monoexponential increase in deoxy-Mb, and this will likely lengthen the myoglobin τ value. Thus, there might be underestimation of τPCr and overestimation of τdeoxy-Mb [see Fig. 2 in Richardson et al. (7)], affecting the conclusion that the adjustment of deoxy-Mb was similar to PCr kinetics. We agree that O2 supply to the active tissues does not determine the rate of adjustment of V˙O2 kinetics during the early phase of exercise. Indeed, recently, we reformulated the “tipping point” concept (4) that Richardson et al. referred to (6) and proposed that when τV˙O2 is less than or equal to approximately 20 s, V˙O2 adjustment is O2-independent (intracellularly controlled). However, our work indicated that when the V˙O2 kinetics response is slower than approximately 20 s, O2 provision to tissues is critical in determining the adjustment of oxidative phosphorylation (4). Although we do not expect Richardson et al. (7) to share our views, we believe that their conclusion that metabolic inertia—not O2 supply—is the major limitation to the full on-transient V˙O2 kinetics is an overstatement and cannot be supported by the data presented. Thus, we would hope that Richardson et al. (7) could accept that alternative views on the topic should have been at least acknowledged. Juan M. Murias Faculty of Kinesiology University of Calgary Calgary, AB CANADA Donald H. Paterson Canadian Centre for Activity and Aging University of Western Ontario London, ON CANADA School of Kinesiology University of Western Ontario London, ON CANADA
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".