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Record W2067000770 · doi:10.1081/ceh-100101997

REDUCTION OF BLOOD PRESSURE VARIABILITY BY AMLODIPINE IN BARORECEPTOR DENERVATED RATS

2000· article· en· W2067000770 on OpenAlexafffund
Bruce N. Van Vliet, L. Chafe

Bibliographic record

VenueClinical and Experimental Hypertension · 2000
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsMemorial University of Newfoundland
FundersMedical Research CouncilPfizer CanadaMemorial University of Newfoundland
KeywordsBaroreceptorAmlodipineBlood pressureMedicineCardiologyInternal medicineReduction (mathematics)BaroreflexAnesthesiaHeart rate

Abstract

fetched live from OpenAlex

To determine the effect of the calcium blocker amlodipine on the variability of mean arterial pressure (MAP), amlodipine besylate was acutely administered to sino-aortic baroreceptor-denervated (SAD) rats (0, 1, 3, 10 mg/kg s.c.), and chronically administered to SAD and sham-denervated rats (0, 50, 150, 500, and 1,500 mg x kg(-1) feed, 4 days per dose). Acute amlodipine administration caused significant dose-dependent reductions of the mean MAP level and short-term MAP variability at the 3 and 10 mg/kg dose levels, respectively. Chronic administration produced dose-dependent reductions in short-term MAP variability, becoming significant at the 150 and 500 mg x kg(-1) feed dose level in SAD and Sham groups, respectively. Day-night differences in blood pressure were significantly attenuated or reversed at the 500 and 1,500 mg x kg(-1) feed dose levels. Amlodipine had little or no effect upon the 24 h MAP level, long-term MAP variability, and only modestly reduced the MAP response to hexamethonium. These results demonstrate that amlodipine can reduce MAP variability independent of changes in the mean blood pressure level.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.301
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2000
Admission routes2
Has abstractyes

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