Bibliographic record
Abstract
The Letter to the Editor by Beltrami (2021) raises questions regarding our recent work that investigated blood flow to the respiratory musculature during exercise and voluntary hyperpnoea (Ramsook et al., 2020). The comments and title of the letter are framed as ‘physiology versus statistics’. We do not view physiology and statistics as competing entities. Rather, our paper uses the time-tested method of presenting raw traces of physiological measures to illustrate the methods used along with the contemporary approach of showing data for individual subjects, mean data, and box-and-whisker plots. Our response will: (i) clarify how we see the physiological complexities of blood flow distribution to the muscles of breathing; and (ii) address selected statistical comments raised. The metabolic and mechanical demands placed on the respiratory muscles can be substantial when ventilation increases above resting levels. The question germane to our work is: does the hyperpnoea of exercise influence the distribution of cardiac output? This question was originally addressed by reducing the work of breathing during heavy exercise with a proportional assist ventilator. When the normally occurring work of breathing was reduced, an increase in leg blood flow was observed (Harms et al., 1997). To investigate this question further, we used a proportional assist ventilator along with near-infrared spectroscopy and an injectable light-absorbing tracer, Indocyanine Green, during cycle exercise to measure blood flow indices of multiple muscles (Dominelli et al., 2017). We found that blood flow to one respiratory muscle (sternocleidomastoid) was reduced and leg blood flow increased when the work of breathing was lowered. Our observations, along with the findings from a series of other studies in humans and experimental animals (Sheel et al., 2018), provide evidence that respiratory muscle work influences the distribution of blood flow to both respiratory and locomotor muscles during exercise. Other researchers have assessed blood flow to the musculature within the seventh intercostal space (for a brief summary, we refer the reader to Sheel et al., 2018) and found a several-fold increase in blood flow with voluntary hyperpnoea while at rest, but a reduction in blood flow below resting levels during exercise when ventilation was increased fourfold. We recognize that there can be sympathetic restraint of blood flow to exercising muscles; however, we are unaware of other reports where contracting skeletal muscle receives a blood flow that is less than that seen at rest. For reasons summarized elsewhere (Sheel et al., 2018), we elected to measure blood flow to the sternocleidomastoid in the present study rather than the intercostal region. The results of this study and of our previous work are in line with the concept that blood is distributed to meet the metabolic demands of the working muscle, whether it is respiratory or locomotor muscle. There is no requirement that interactions be tested after main effects, although they lack interpretability in the absence of main effects in most settings. It is not uncommon to consider assessing the significance of interactions when there are important main effects (the so-called ‘effect heredity principle’), for example, in screening experiments (Wu & Hamada, 2009). When we refit the full model, we found results that were consistent with those already reported in the paper. We take the opportunity to conclude by returning to the physiological rationale for our study and point to an important and still unresolved question: is sympatholysis during heavy-intensity exercise greater in the diaphragm than in limb locomotor muscles? Insight into this complex problem would benefit from technological advances that permit the measurement of blood flow to the diaphragm in addition to an understanding of the responsiveness and adrenergic receptor densities of diaphragm versus locomotor muscle vasculatures. Addressing these complexities will require a physiological and statistical approach.
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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 | 0.008 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.026 | 0.036 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".