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Clonidine, dexmedetomidine: alpha-2 adrenergic receptor agonists in neuroscience

2018· article· en· W2901639573 on OpenAlexaff
Jabril Eldufani, Nyruz Elahmer, Alireza Nekoui, Gilbert Blaise

Bibliographic record

VenueInternational Journal of Basic & Clinical Pharmacology · 2018
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsDexmedetomidineClonidineNeurochemicalMedicineAlpha-2 adrenergic receptorNeuroscienceChronic painDepression (economics)Adrenergic receptorAlpha (finance)PharmacologyPsychiatryReceptorAnesthesiaPsychologyInternal medicineClinical psychology

Abstract

fetched live from OpenAlex

The alpha-2 adrenergic receptor (α-2 AR) agonists have a long history of use in treating different clinical conditions, such as hypertension, psychiatric entities (e.g., attention-deficit hyperactivity disorder), chronic pain, panic disorders, and, lately, for treating opioid withdrawal syndrome. In recent years, α-2 AR medications have been administered as adjuncts for managing inflammatory conditions, depression, chronic pain, sleep and cognitive disorders. This review will provide some clinical applications in neuroscience for this class of drugs. Understanding the pharmacological mechanisms is essential to obtaining neurochemical data that demonstrate that α-2 AR agonists have potential clinical significance in neuroscience.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.061
GPT teacher head0.462
Teacher spread0.401 · 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.

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

Citations2
Published2018
Admission routes1
Has abstractyes

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