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Record W2115371929 · doi:10.1113/eph8702434

Renal Sympathetic Nerves do not Modulate Renal Responses to Haemorrhage in Conscious Lambs

2002· article· en· W2115371929 on OpenAlexaff
Francine G. Smith

Bibliographic record

VenueExperimental Physiology · 2002
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsUniversity of Calgary
FundersMedical Research Council
KeywordsSympathetic nervous systemMedicineKidneyInternal medicineEndocrinologyBlood pressure

Abstract

fetched live from OpenAlex

The present study was designed to investigate the renal responses to hypotensive haemorrhage early in life and the role of renal sympathetic nerves in modulating these renal responses. To this end, experiments were carried out in conscious, chronically instrumented lambs with either intact renal nerves (n = 7, Intact) or bilateral renal denervation performed at the time of surgery (n = 5, Denervated). Parameters of renal function were measured before and after 20% haemorrhage (experiment 1) and 0% haemorrhage (experiment 2), the latter serving as a time control. The two experiments were performed in random order at intervals of 24-48 h. Within 20 min of hypotensive haemorrhage in intact lambs, glomerular filtration rate decreased by approximately 60%; this response was not altered by renal denervation. Since renal plasma flow remained constant after haemorrhage, the filtration fraction also decreased. After 20% haemorrhage, urinary flow rate decreased in intact lambs; this response was also not altered by renal denervation. Excretion rates of Na+ and K+ as well as urinary osmolality and free water clearance were not altered by haemorrhage in either intact or denervated lambs. These data provide the first description of renal responses to haemorrhage early in life. In addition, the present findings provide new information that renal responses to haemorrhage early in life do not appear to be modulated by renal sympathetic nerves.

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 score0.005
Threshold uncertainty score0.812

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)0.0010.001

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 head0.051
GPT teacher head0.304
Teacher spread0.253 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

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