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Record W4404179327 · doi:10.4088/jcp.plunaro2417ah

Dysregulation of Noradrenergic Activity

2024· review· en· W4404179327 on OpenAlexafffund
Rakesh Jain, Craig Chepke, Lori L. Davis, Roger S. McIntyre, Murray A. Raskind

Bibliographic record

VenueThe Journal of Clinical Psychiatry · 2024
Typereview
Languageen
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersH. Lundbeck A/SCanadian Institutes of Health ResearchTeva Pharmaceutical IndustriesBiogenNational Natural Science Foundation of ChinaU.S. Department of Defense
KeywordsPsychologyNeuroscienceMedicine

Abstract

fetched live from OpenAlex

When discussing neurotransmitters whose signaling plays an important role in psychiatric illnesses, serotonin and dopamine may be the first that come to mind. Although serotonin and dopamine have significant roles, the impact of norepinephrine signaling is often overlooked. A growing body of evidence suggests that hyperactivity of norepinephrine signaling is an underlying issue in psychiatric disorders; conversely, there is evidence to suggest that deficits in the noradrenergic system are just as significant. Hence, alterations in noradrenergic activity are better characterized as dysregulation rather than a reductive, outdated formulation of "too much" or "too little" activity. Therefore, symptoms such as agitation, irritability, hyperarousal, and insomnia could be treated by targeting the underlying pathophysiology related to noradrenergic dysregulation with targeted treatments. In a recent consensus panel meeting, 5 experts reviewed the available evidence of altered noradrenergic activity and its potential role in some of the most common psychiatric disorders. This Academic Highlights article summarizes their discussion and presents the panel's conclusions.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.228
GPT teacher head0.525
Teacher spread0.297 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations18
Published2024
Admission routes2
Has abstractyes

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