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Stair use for cardiovascular disease prevention

2009· review· en· W2118372585 on OpenAlexaff
Philippe Meyer, Bengt Kayser, François Mach

Bibliographic record

VenueEuropean Journal of Cardiovascular Prevention & Rehabilitation · 2009
Typereview
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsMontreal Heart InstituteUniversité de Montréal
Fundersnot available
KeywordsMedicineDiseasePhysical therapyPhysical medicine and rehabilitationIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Introduction There is no doubt that higher physical activity and fitness levels are inversely associated with the risk of cardiovascular disease (CVD) [1]. Yet, most Europeans and Americans do not meet current minimum physical activity recommendations [2] and the prevalence of CVD risk factors related to a sedentary lifestyle, such as obesity and diabetes, is increasing at an alarming rate [3,4]. To counter those trends, there is an urgent need to develop population strategies aiming to increase physical activity. Stair use is inexpensive and can be easily integrated into everyday life by most of the population. Stair climbing represents vigorous-intensity physical activity with oxygen uptake reaching approximately 80% of maximal values in young healthy adults, corresponding up to nearly 10 metabolic equivalents (METs) of energy expenditure [5], sufficient to improve cardiorespiratory fitness [6]. In the Harvard College alumni cohort study, stair climbing was associated with a significant decrease in mortality risk of Z 21% for people climbing Z 20 floors compared with those climbing less than 20 floors per week [1]. The purpose of this study is to review studies that evaluated the effects of stair use on fitness and CVD risk factors, present results of the recent Geneva Stair study, and finally formulate general conclusions on the promotion of stair use for CVD prevention at a population level.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.028
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.096
GPT teacher head0.363
Teacher spread0.267 · 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 designOther design
Domainnot available
GenreReview

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

Citations29
Published2009
Admission routes1
Has abstractyes

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