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Record W2962103152 · doi:10.1113/jp278328

Rebuttal from Martin MacInnis, Lauren Skelly and Martin Gibala

2019· letter· en· W2962103152 on OpenAlexafffundabout
Martin J. MacInnis, Lauren E. Skelly, Martin J. Gibala

Bibliographic record

VenueThe Journal of Physiology · 2019
Typeletter
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsMcMaster UniversityUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHigh-intensity interval trainingInterval trainingSprintMedicineContinuous trainingCitrate synthaseIntensity (physics)Physical therapyInternal medicineAnimal scienceBiology

Abstract

fetched live from OpenAlex

We commend Bishop et al. (2019) for their comprehensive summary but respectfully disagree with their conclusion that training volume is more important than exercise intensity for increasing mitochondrial content in human skeletal muscle. Their position, based on the compiled data, centres on: (1) a positive correlation between training volume and citrate synthase (CS) activity and mitochondrial volume density (MitoVD); and (2) no correlation between training intensity and CS activity or MitoVD. The analysis presented by Bishop et al. (2019) is problematic for isolating the importance of exercise intensity per se. Firstly, on average, ∼37% more training sessions were performed for the moderate-intensity continuous training (MICT) as compared to high-intensity interval training (HIIT) and sprint interval training (SIT) (Granata et al. 2018): the HIIT and SIT interventions involved ∼15 sessions (lasting ∼15 min) compared to 21 sessions (lasting ∼70 min) for MICT (Granata et al. 2018). As indicated by time-course data (e.g. Murias et al. 2011), mitochondrial content increases as a training intervention progresses, which complicates comparisons among the intervention types. Secondly, despite the interval protocols being performed at higher intensities, the training volume for MICT (n = 33) was ∼60% and ∼700% greater than for HIIT (n = 9) and SIT (n = 20), respectively (Granata et al. 2018). As Bishop et al. (2019) demonstrated that training volume was positively associated with changes in mitochondrial content, the similar increase in CS activity across training types, despite differences in training volume, is strong evidence that HIIT and SIT were more efficient than MICT for increasing mitochondrial content. Given the disparities across intervention types, the lack of correlation between exercise intensity and the change in mitochondrial content is unsurprising and is not evidence against the importance of exercise intensity. Finally, while we agree that studies comparing different training protocols are important (Daussin et al. 2008; Granata et al. 2016; MacInnis et al. 2017; Montero & Lundby, 2017; Shepherd et al. 2017), we wish to clarify that the training interventions employed by Gillen et al. (2016) elicited similar increases in CS activity despite SIT involving about one-fifth the total volume of MICT (i.e. not equal volumes), demonstrating that high intensities of exercise can ‘compensate’ for low training volumes. Thus, in addition to our initial arguments (MacInnis et al. 2019), we contend that the pooled analysis from Bishop et al. (2019) supports our position that training intensity is more important than training volume for increasing human skeletal muscle mitochondrial content. Readers are invited to give their views on this and the accompanying CrossTalk articles in this issue by submitting a brief (250 word) comment. Comments may be submitted up to 6 weeks after publication of the article, at which point the discussion will close and the CrossTalk authors will be invited to submit a ‘LastWord’. Please email your comment, including a title and a declaration of interest, to [email protected] Comments will be moderated and accepted comments will be published online only as ‘supporting information’ to the original debate articles once discussion has closed. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. The authors declare that they have no competing interests. M.J.M., L.E.S. and M.J.G. contributed equally to the writing and critical revision of this manuscript. 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. The research programmes of M.J.M. (ID: RGPIN-2018-06424) and M.J.G. (ID: RGPIN-2015-04632) are funded through Discovery Grants from the Natural Sciences and Engineering Research Council of Canada (NSERC). L.E.S. was funded by an NSERC Vanier Canada Graduate Scholarship.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.066
Threshold uncertainty score0.222

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.043
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0050.005
Open science0.0030.005
Research integrity0.0080.014
Insufficient payload (model declined to judge)0.0660.072

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.

Opus teacher head0.009
GPT teacher head0.232
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2019
Admission routes3
Has abstractyes

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