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Record W2345366908 · doi:10.1177/0148607115619598

Effect of Intravenous Infusion Solutions on Bioelectrical Impedance Spectroscopy

2015· article· en· W2345366908 on OpenAlexafffund
Jason Yap, Mahroukh Rafii, Maria Azcue, Paul B. Pencharz

Bibliographic record

VenueJournal of Parenteral and Enteral Nutrition · 2015
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsUniversity of TorontoHospital for Sick ChildrenUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsBioelectrical impedance analysisMedicineAnesthesiaIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Bioelectrical impedance (BIA) is often used to measure body fluid spaces and thereby body composition. However, in acute animal studies, we found that impedance was driven by the saline content of intravenous (IV) fluids and not by the volume. The aim of the study was to investigate the effect of 3 different fluids acutely administered on the change in impedance, specifically resistance (R). MATERIALS AND METHODS: Nine healthy adults participated in 3 treatment (0.9% saline, 5% dextrose, and a mixture of 0.3% saline + 3.3% dextrose) experiments on nonconsecutive days. They all received 1 L of one of the treatments intravenously over a 1-hour period. Repeated BIA measurements were performed prior to IV infusion and then every 5 minutes for the 1-hour infusion period, plus 3 more measurements up to 15 minutes after the completion of the infusion. RESULTS: The change in R in the 0.9% saline infusion experiment was significantly lower than that of the glucose and mixture treatment ( P < .001). CONCLUSION: Bioelectrical impedance spectroscopy and BIA measure salt rather than the volume changes over the infusion period. Hence, in patients receiving IV fluids, BIA of any kind (single frequency or multifrequency) cannot be used to measure body fluid spaces or body composition.

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)0.000
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 score0.105
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.028
GPT teacher head0.312
Teacher spread0.284 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

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