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Record W2202357822 · doi:10.5539/ijb.v8n1p69

Microdialysis Sampling of Renal Interstitial Fluid in Acute Studies

2015· article· en· W2202357822 on OpenAlexvenueno aff
Wael A. Alanazi, Selim Fakhruddin, Keith E. Jackson

Bibliographic record

VenueInternational Journal of Biology · 2015
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsnot available
FundersSaudi Arabian Cultural MissionNational Institutes of HealthLouisiana Biomedical Research Network
KeywordsMicrodialysisInterstitial fluidSampling (signal processing)In vivoKidneyBiomedical engineeringMedicineComputer sciencePathologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

<p class="1Body">The microdialysis technique has been applied extensively during the last three decades where most of the research has focused primarily on the site of action. The microdialysis probe was developed for collection of interstitial fluid as well as delivering drugs to the target site. In comparison with other methods, the microdialysis technique has delivered more accurate results with minimal invasive procedures. Initially, the microdialysis technique was designed to quantitate dopamine levels in cerebral tissues, which facilitated the applications of this technology as a sampling tool. Presently the microdialysis technique has been applied to various organs such as the heart, kidney, and liver. In the current report, we describe the principle of the microdialysis technique and its application in <em>in-vivo</em> studies. Specifically, the present review provides the adaptation of this method in renal acute studies. However, the rationale for this report is to demonstrate that the microdialysis technique can be used to collect renal interstitial fluid in real time with no effect on the hemodynamic function.</p>

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)0.003
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 score0.087
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 head0.116
GPT teacher head0.396
Teacher spread0.280 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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