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Record W2194035405

The reliability and sensitivity of indices related to cardiovascular fitness evaluation

2008· article· en· W2194035405 on OpenAlexaff
Jinzhou Yuan, Yibing Fu, Ruipeng Zhang, Li Xi, Gongbing Shan

Bibliographic record

VenueUniversity of Zagreb University Computing Centre (SRCE) · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsReliability (semiconductor)Test (biology)Step testSensitivity (control systems)Index (typography)PopulationStatisticsSquatFitness testCardiovascular fitnessVO2 maxPhysical fitnessReliability engineeringMathematicsPhysical therapyMedicineHeart rateComputer scienceInternal medicineEngineeringSignificant differenceBlood pressureEnvironmental healthBiology
DOInot available

Abstract

fetched live from OpenAlex

Determining the recovery heart rate (RHR) index after various submaximal exercises is a popular and practical way for a population’s cardiovascular fitness evaluation. These evaluations are based on the regression among RHR, load intensity, and maximal oxygen uptake. However, little work has been done to 1) explore the influences of body weight and height on these tests’ reliability and sensitivity, and 2) compare the reliability among the tests. As a result, practitioners often choose tests without the appropriate criteria. This study researched the mentioned two aspects by evaluating 30 male college students via three common tests – 30 cm step test, 40 cm step test and squat-up-down test. The results showed that the reliability and sensitivity of the three tests were remarkably different. Adding body weight into the evaluation would improve both reliability and sensitivity. Considering all the influence factors, 30 cm step test was the best one. These findings suggested that applying the relative RHR index (normalized by body weight) should be considered for a population’s cardiovascular fitness evaluation in the future.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.196
Teacher spread0.188 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2008
Admission routes1
Has abstractyes

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