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Record W1999792938 · doi:10.1139/y04-028

Cardiovascular and parasympathetic effects of dexmedetomidine in healthy subjects

2004· article· en· W1999792938 on OpenAlexvenueno aff
Jani Penttilä, Antti Helminen, Markku Anttila, Susanna Hinkka‐Yli‐Salomäki, Harry Scheinin

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2004
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsnot available
Fundersnot available
KeywordsDexmedetomidineHeart rateMedicineAnesthesiaBlood pressureBuccal administrationBaroreflexEfferentAgonistInternal medicineSedationPharmacology

Abstract

fetched live from OpenAlex

We evaluated the cardiovascular effects of intravenously (i.v.) and buccally administered dexmedetomidine, a selective alpha2-adrenoceptor agonist. Six healthy male subjects were studied unmedicated and after 2 micro g/kg i.v. or buccal doses of dexmedetomidine, using repeated recordings of ECG and blood pressure. Cardiac parasympathetic activity was estimated by measurements of high-frequency (HF) heart rate variability. Intravenous, but not buccal, dexmedetomidine raised systolic blood pressure by 11 +/- 5 mmHg (mean +/- SEM) and diastolic by 16 +/- 3 mmHg (maxima at 10 min). Later on, both i.v., and buccal dexmedetomidine produced a very similar hypotensive effect: on average, >or=10 mmHg reductions in systolic and diastolic pressure at 3 h. Intravenous dosing was followed by a decline in heart rate (-11 +/- 2 beats/min) accompanied by a trend toward enhanced HF variability (maximal effect at 10 min), which probably reflected baroreflex-mediated parasympathetic efferent neuronal activation. Buccal dexmedetomidine increased significantly the HF variability (maximum at 45 min) without influencing heart rate. We conclude that dexmedetomidine, when administered by a method that avoids concentration peaks, e.g., buccal dosing, can be used to produce a prolonged augmentation of cardiac parasympathetic efferent neuronal activity.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.292

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 head0.012
GPT teacher head0.268
Teacher spread0.257 · 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

Citations78
Published2004
Admission routes1
Has abstractyes

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