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Record W1500150694 · doi:10.1113/jphysiol.2014.284406

Rebuttal from Stuart M. Phillips and Chris McGlory

2014· letter· en· W1500150694 on OpenAlexafffundabout
Stuart M. Phillips, Chris McGlory

Bibliographic record

VenueThe Journal of Physiology · 2014
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchCanadian Diabetes Association
KeywordsArgument (complex analysis)RebuttalProteolysisMechanism (biology)BiologyPhilosophyEvolutionary biologyEpistemologyBiochemistryHistory

Abstract

fetched live from OpenAlex

In our opponents’ view (Reid et al. 2014), the exact answer to the question of what is the dominant mechanism remains unknown and, as they conclude, ‘…proteolysis is an essential component of disuse atrophy.’ Importantly, we agree with both points. However, our position remains that the answer as to which is the dominant mechanism cannot merely be obtained from static protein/gene measurements or from rodent knock out models. As we in our original manuscript, and others (Cunningham, 2002) have highlighted, there are marked interspecies differences between humans and rodents that we propose have a significant bearing on this debate. While important proof of concept mechanistic information can be derived from rodent models they are still methodologically flawed. For example, the continued use of ex vivo muscle preparation is inherently biased towards showing elevated rates of muscle protein breakdown (MPB) and proteolytic markers as the tissue is essentially dying (albeit slowly) during the experiment. Our opponents cite only a single human study that attempted to measure MPB to support their argument (Tesch et al. 2008), which relied on an indirect proteolytic marker, and which did not include concomitant assessments of muscle protein synthesis (MPS). Even in recent papers (Bodine & Baehr, 2014) where large volumes of data were reviewed, in very few cited studies were actual rates of protein synthesis and/or breakdown even reported; instead, the change in muscle mass was estimated, proteolytic ‘markers’ were measured and a ‘conclusion’ reached as to the mechanism. To clarify, we do not dismiss our opponents’ argument that at least in the early phase of muscle disuse (<10 days), increases in MPB contribute to the decline in skeletal muscle size (Wall et al. 2013). There is evidence (with indirect markers) for this assertion, even in humans (Tesch et al. 2008). Nonetheless, the decline in human muscle size with disuse is predominantly driven by a reduction in the rate of MPS, especially in response to contraction and nutrition. Our opponents’ use of a graphic word cloud to support their thesis, while intriguing, is not in our view evidence that they are correct. Instead, we view this graphic ‘evidence’ as a depiction of the number of animal- versus human-based researchers in this area. Since animal-based researchers have generated most of the data in this field, the use of a word cloud in this instance is simply a reflection of the conclusion of the majority of researchers, using the same model, in this field. Thus, the word cloud is analogous to someone shouting the loudest in an argument and believing that approach renders their argument correct. 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 ‘Last Word’. Please email your comment to journals@physoc.org. Disclaimer: Supplementary materials have been peer-reviewed but not copyedited. 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 report no conflict of interest, financial or otherwise. This work was supported by grants to S.M.P. from the National Science and Engineering Research Council of Canada and the Canadian Institutes of Health Research, as well as the Canadian Diabetes Association.

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)0.010
metaresearch head score (Gemma)0.057
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 score0.029
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.057
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0090.013
Open science0.0050.006
Research integrity0.0290.065
Insufficient payload (model declined to judge)0.0200.023

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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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

Citations7
Published2014
Admission routes3
Has abstractyes

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