Reply from P. Dominelli, C. Wiggins, S. E. Baker, J. R. A. Shepherd, S. Roberts, T. K. Roy, T. Curry, J. Hoyer, J. L. Oliveira and M. J. Joyner
Bibliographic record
Abstract
The authors raise several important points regarding our recent publication (Dominelli et al. 2020). Prior to addressing their points below, we would like to emphasize that the purpose and primary research question revolved around the impact of high-affinity haemoglobin (HAH) on the response to exercise (primarily maximal oxygen uptake) and pulmonary gas exchange. While the points raised by the authors are important and warrant further discussion, they have little bearing on our main conclusions. The average Hill's n was 2.73 and 1.97 for controls and HAH subjects, respectively. We agree with the authors that there are other important variables other than P50 that have and impact on the oxygen cascade and this has been further highlighted by Dempsey et al. (1975), which we also discuss in our paper. It is important to clarify that our group was quite homogenous in terms of variant haemoglobin (all Hb Malmö except one subject), thus there was little variability in the shape of the ODC. Furthermore, our primary question related to HAH impact on exercise and not the specific mechanism responsible. We agree that going forward when different haemoglobin variants are available and we are testing the specific mechanisms behind the enhanced exercise performance it is important to consider other characteristics of the ODC and not just the P50. Regarding the measurements of the ODC, we very much agree with the authors that in vitro measures of the ODC are not wholly representative of in vivo conditions and this is especially true during dynamic exercise. We discuss this, in part, under the Anaerobic metabolism section. Given that we performed maximal exercise tests, which result in considerable variations in pH, temperature and CO2, in both normoxic and hypoxic conditions, we would have had an unmanageable number of combinations of factors that can affect the ODC. Given logistical constraints, it was not possible to test all these combinations and the analysis was performed in a standardized manner. We did perform additional ODC measures with a pH of 7.2 in a control and HAH (detailed in the Discussion) and found that the change in P50 was relatively similar. We acknowledge the authors’ point that there are many other factors that could potentially alter the shape of the ODC, but again, this was not the purpose of this study. Regarding the scattering of data due to potential differences in the subject's weight, body mass index and peak power, each of these is already addressed in the manuscript. For example, we acknowledge that although “not statistically different”, aerobic fitness (used here as a surrogate for peak power) “appeared to be lower in those with HAH” and we discuss the implications. The authors are correct in that the process developed by Winslow (Winslow et al. 1977) predates the Hemox analyzer. Rather, the clinical protocol developed at our institution was based on this work and provide consistent results used for clinical diagnosis. Regarding the non-standardized pedal frequency, although we did not force cadence to specific values, all subjects maintained a pedal frequency between 60 and 85 rpm. We note from the authors’ cited reference that there is little variance in many parameters (especially at higher workloads) in this range of cadence. This is also consistent with a more recent publication reporting minimal effects across our range of cadences (Mitchell et al. 2019). We certainly agree that pedalling at extreme cadences (e.g. under 40 or above 120 rpm) would result in differing efficiency, but this is not the range relevant to our subjects. Furthermore, our primary question was the difference between normoxic and hypoxic exercise in each subject. As such, whatever cadence was adopted in the first exercise bout we had the subject maintain in the second. Regarding our lactate interpretation, we acknowledge in the discussion that the ‘lactate paradox’ is observed in “acclimatized subjects” but that the issue is “less clear during acute exposure”. We brought forward the idea of the lactate paradox simply to convey how lactate metabolism (and acid-base balance) is a complex and sometimes perplexing topic especially in the presence of hypoxia. We also appreciate that there are many factors that may have led to the decrease in lactate in the control subjects and a higher relative intensity could be one explanation. We again emphasize that determining how lactate metabolism potentially differs in subjects with high-affinity haemoglobin was not the objective of the current study. Such an undertaking would require a different experimental design and methods that would hinder our ability to determine how maximal oxygen uptake was impacted. Regarding base excess, our calculation of base excess was the actual base excess and was derived from standard equations that account for different buffering capacities of haemoglobin. Our intention was to convey that the polycythaemia associated with the haemoglobin variants contributed to the base excess, as suggested in the letter. None. All authors have read and approved the final version of this manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. None.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.023 | 0.034 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".