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A model of fluid and solute exchange in the human: validation and implications

2000· article· en· W4242642684 on OpenAlexaff
Joel L. Bert, Christina C. Gyenge, B.D. Bowen, Rolf K. Reed, T. Lund

Bibliographic record

VenueActa Physiologica Scandinavica · 2000
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsChemistry

Abstract

fetched live from OpenAlex

In order to understand better the complex, dynamic behaviour of the redistribution and exchange of fluid and solutes administered to normal individuals or to those with acute hypovolemia, mathematical models are used in addition to direct experimental investigation. Initial validation of a model developed by our group involved data from animal experiments (Gyenge, C.C., Bowen, B.D., Reed, R.K. & Bert, J.L. 1999b. Am J Physiol277 (Heart Circ Physiol46), H1228–H1240). For a first validation involving humans, we compare the results of simulations with a wide range of different types of data from two experimental studies. These studies involved administration of normal saline or hypertonic saline with Dextran to both normal and 10% haemorrhaged subjects. We compared simulations with data including the dynamic changes in plasma and interstitial fluid volumes VPL and VIT respectively, plasma and interstitial colloid osmotic pressures ΠPL and ΠIT respectively, haematocrit (Hct), plasma solute concentrations and transcapillary flow rates. The model predictions were overall in very good agreement with the wide range of experimental results considered. Based on the conditions investigated, the model was also validated for humans. We used the model both to investigate mechanisms associated with the redistribution and transport of fluid and solutes administered following a mild haemorrhage and to speculate on the relationship between the timing and amount of fluid infusions and subsequent blood volume expansion.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.205

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 head0.063
GPT teacher head0.313
Teacher spread0.250 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

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