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Record W4399767351 · doi:10.54097/pfn79745

Examining the Effect of Cognitive Behavioral Therapy on Anxiety Disorders from a Neurological Perspective

2024· article· en· W4399767351 on OpenAlexaff
Sichen Dong

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2024
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsMcGill University
Fundersnot available
KeywordsAnxietyNeuroimagingCognitionPerspective (graphical)PsychologyPsychotherapistFunctional magnetic resonance imagingPositron emission tomographyMagnetic resonance imagingClinical psychologyNeuroscienceMedicinePsychiatryRadiology

Abstract

fetched live from OpenAlex

Cognitive Behavioral Therapy (CBT) stands out as an effective approach for addressing anxiety disorders. The advancement of neuroimaging technologies, including Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Functional Magnetic Resonance Imaging (fMRI), has provided scientists with invaluable tools to delve into the study of mental disorders. This essay aims to explore recent evidence supporting the neurological basis of CBT's effectiveness in treating anxiety disorders. Beginning with an introduction to the concept of emotion regulation and cognitive fear structure, this essay proceeds to analyze experimental results, elucidating the fundamental mechanisms underlying anxiety disorders and presenting compelling scientific evidence attesting to the efficacy of CBT.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.320
Teacher spread0.299 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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