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Record W2911736014 · doi:10.1093/neuros/nyy635

Amygdala and Hypothalamus: Historical Overview With Focus on Aggression

2019· article· en· W2911736014 on OpenAlexaff
Flavia Venetucci Gouveia, Clement Hamani, Erich Talamoni Fonoff, Helena Brentani, Eduardo Joaquim Lopes Alho, Rosa M. C. B. Morais, Aline Luz de Souza, Sergio P. Rigonatti, Raquel Chacon Ruiz Martinez

Bibliographic record

VenueNeurosurgery · 2019
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineAggressionAmygdalaNeurosurgeryDeep brain stimulationHypothalamusNeuroscienceIntervention (counseling)PopulationPsychiatryInternal medicinePsychology

Abstract

fetched live from OpenAlex

Aggressiveness has a high prevalence in psychiatric patients and is a major health problem. Two brain areas involved in the neural network of aggressive behavior are the amygdala and the hypothalamus. While pharmacological treatments are effective in most patients, some do not properly respond to conventional therapies and are considered medically refractory. In this population, surgical procedures (ie, stereotactic lesions and deep brain stimulation) have been performed in an attempt to improve symptomatology and quality of life. Clinical results obtained after surgery are difficult to interpret, and the mechanisms responsible for postoperative reductions in aggressive behavior are unknown. We review the rationale and neurobiological characteristics that may help to explain why functional neurosurgery has been proposed to control aggressive behavior.

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)0.001
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.019
GPT teacher head0.265
Teacher spread0.245 · 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

Citations99
Published2019
Admission routes1
Has abstractyes

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