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Record W4386663620 · doi:10.54434/candj.76

Cardiorespiratory Fitness Assessment and Treatment for Health Span and Lifespan

2021· article· en· W4386663620 on OpenAlexaffvenue
David Duizer

Bibliographic record

VenueCAND Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsSpinal Cord Injury BC
Fundersnot available
KeywordsCardiorespiratory fitnessMedicinePhysical therapyVO2 maxDiseaseDementiaPopulationPhysical fitnessGerontologyPhysical medicine and rehabilitationInternal medicineBlood pressureHeart rateEnvironmental health

Abstract

fetched live from OpenAlex

Poor cardiorespiratory fitness (CRF) is an important risk factor for age-related diseases including cardiovascular disease, cancer, dementia and osteoporosis. When evaluated through an active metabolism test and reported as VO2Max, or the maximal oxygen uptake during intense exercise, CRF is a highly valued functional marker to assess overall wellness and disease risk in the elderly. For every 3.5ml/kg/min increase in VO2Max there is an associated 13% decrease in all-cause mortality. CRF is trainable in the elderly through endurance and high intensity interval training. A Naturopathic Doctor’s holistic approach to optimal wellness includes exercise as a lifestyle intervention and CRF analysis enables their exercise protocols to be individualized for the purpose of improving VO2Max. VO2Max can be improved by up to 11% in 8 weeks with exercise consistency. CRF evaluation and treatment in the elderly population is a high priority of an evidence-informed optimal wellness program and one that Naturopathic Doctors are well trained to lead.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.036
GPT teacher head0.358
Teacher spread0.322 · 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
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

Citations1
Published2021
Admission routes2
Has abstractyes

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