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High &OV0312;O2max with no history of training is primarily due to high blood volume

2002· article· en· W2058543438 on OpenAlexaff
Marco De Martino, Norman Gledhill, Veronica Jamnik

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2002
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsYork University
Fundersnot available
KeywordsMedicineInternal medicineStroke volumeVO2 maxCardiologyAnimal scienceHeart rateBlood pressureBiology

Abstract

fetched live from OpenAlex

PURPOSE: To investigate the high VO2max observed occasionally in young men who have no history of training. METHODS: VO2max, blood volume (BV), maximal stroke volume (SVmax), maximal cardiac output (Qmax), and related measurements (reported as mean +/- SEM) were studied in six men (mean age 20.0 +/- 0.5 yr) with no history of training, who all had a VO2max below 49 mL.kg-1.min-1 (LO group) and six age- and weight-matched men (mean age 19.5 +/- 0.5 yr) with no history of training, who all had a VO2max above 62.5 mL.kg-1.min-1 (HI group). RESULTS: Compared with the LO group, the HI group had a higher SVmax (149 +/- 5 vs 102 +/- 5 mL), higher Qmax (28.9 +/- 0.9 vs 20.0 +/- 1.0 L.min-1) and higher BV (88.1 +/- 3.8 vs 76.7 +/- 0.9 mL.kg-1). The BV of four participants in the HI group (mean = 92.3 +/- 4.3 mL.kg-1) was substantially higher than the BV of all participants in the LO group, but two participants in the HI group had a BV (mean = 79.7 +/- 0.8 mL.kg-1) that was similar to the mean BV of the LO group. CONCLUSION: The primary explanation for the high VO2max observed occasionally in young men who have no history of training is a naturally occurring (perhaps genetically determined) high BV that brings about a high SVmax and Qmax. However, some young men with no history of training have a high VO2max, SVmax, and Qmax possibly because a greater portion of their BV is hemodynamically active.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.234
Teacher spread0.215 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations62
Published2002
Admission routes1
Has abstractyes

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