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Record W2040311321 · doi:10.1016/s0924-9338(10)70360-6

P01-155 - Treatment Outcome and Predictors of Response in Canadian Combat and Peacekeeping Veterans with Military-related PTSD

2010· article· en· W2040311321 on OpenAlexaffabout
Julie Richardson, Jon D. Elhai, Jitender Sarreen

Bibliographic record

VenueEuropean Psychiatry · 2010
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversity of ManitobaWestern University
Fundersnot available
KeywordsDepression (economics)AnxietyPsychiatryPeacekeepingPosttraumatic stressVeterans AffairsClinical psychologyPsychologyMilitary personnelMedicineInternal medicine

Abstract

fetched live from OpenAlex

Objective This study examined the initial clinical presentation and monthly re-assessments of treatment outcome for a group of combat veterans and deployed peacekeepers with posttraumatic stress disorder (PTSD). Method Participants were 102 Canadian combat and peacekeeping Veterans attending specialized PTSD treatment clinic (Operational Stress Injury Clinic). Results/conclusions Help seeking veterans with PTSD presented with significant comorbid major depressive disorder and results at 12 months demonstrated significant improvement in symptoms of PTSD, depression and anxiety. There was no significant predictor of PTSD symptoms decline. Initial depression significantly predicted anxiety symptom declines, and initial anxiety predicted depression symptom declines. There was also a demonstrable improvement in health-related quality of life as measured by the SF 36.

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 score0.235
Threshold uncertainty score0.472

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.314
Teacher spread0.292 · 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

Citations1
Published2010
Admission routes2
Has abstractyes

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