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Record W2029386196 · doi:10.1080/10913671003715607

Effects of Ventilation on Segmental Bioimpedance Spectroscopy Measures Using Generalizability Theory

2010· article· en· W2029386196 on OpenAlexaff
A. Allan Turner, Albert Lozano‐Nieto, Marcel Bouffard

Bibliographic record

VenueMeasurement in Physical Education and Exercise Science · 2010
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeneralizability theoryVentilation (architecture)TrunkReproducibilityForearmExtracellular fluidFacet (psychology)MedicinePhysical therapyPsychologyExtracellularMathematicsStatisticsChemistryAnatomyBiologyBiochemistry

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the effect of three ventilation conditions (i.e., normal, regimented, and no-ventilation) on the reproducibility of bioimpedance scores in humans for the forearm and trunk segments. One hundred able-bodied North American men and women, from 18 to 71 years of age, volunteered as participants. The experimenters used a Xitron Bio-Impedance Analyzer System model 4200 instrument with Hydra software (Xitron Technologies, San Diego, California, USA) to collect bioimpedance data on extracellular fluid and intracellular fluid scores. The experimenters analyzed the data using the generalizability theory,with persons as the facet of differentiation and time as the facet of generalization. The major findings were (a) ventilation conditions did not have a significant impact on the reproducibility of the test scores, (b) the forearm segment produced consistently higher intracellular fluid generalizability coefficients across three ventilation conditions for both gender groups when compared to the trunk segment, (c) the trunk segment produced intracellular fluid generalizability coefficients that were higher for the male group, and (d) the measurement error affected extracellular fluid scores less than segmental intracellular fluid scores.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.326
Teacher spread0.305 · 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 designBench or experimental
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

Citations0
Published2010
Admission routes1
Has abstractyes

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