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Record W2115884196 · doi:10.1139/jpn.0816

Rostral anterior cingulate volume predicts treatment response to cognitive-behavioural therapy for posttraumatic stress disorder

2008· article· en· W2115884196 on OpenAlexvenueno aff
Richard A. Bryant, Kim L. Felmingham, Thomas J. Whitford, Andrew H. Kemp, Gerard Hughes, Anthony Peduto, Leanne M. Williams

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2008
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsAnterior cingulate cortexPosttraumatic stressPsychologyMagnetic resonance imagingCognitionCognitive behavioral therapyClinical psychologyVoxelAmygdalaMedicineInternal medicineNeuroscienceRadiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To index the extent to which treatment response in posttraumatic stress disorder (PTSD) is predicted by rostral anterior cingulate cortex (rACC) volume. METHOD: We used structural magnetic resonance imaging in a 1.5 T scanner to examine subjects with PTSD (n = 13), traumatized control subjects (n = 13) and nontraumatized control subjects (n = 13). Subjects with PTSD then participated in 8 sessions of cognitive-behavioural therapy, after which we reassessed them for PTSD. RESULTS: According to voxel-based morphometry, treatment responders had larger rACC volume than nonresponders. Further, symptom reduction was associated with larger rACC volume. CONCLUSION: Consistent with evidence for the neural bases of extinction learning, PTSD patients with larger rACC volume may be better able to regulate fear during cognitive-behavioural therapy and thus achieve greater treatment gains.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.091
GPT teacher head0.382
Teacher spread0.291 · 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 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

Citations134
Published2008
Admission routes1
Has abstractyes

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