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Outcomes in a Referral Cohort of Patients with Anxiety Disorders

2000· article· en· W2335149855 on OpenAlexaff
Jane McCusker, Jean‐Philippe Boulenger, François Bellavance, Richard Boyer, Jean‐Marc Miller

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2000
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsMcGill UniversityInstitut universitaire en santé mentale de MontréalSt Mary's Hospital Centre
Fundersnot available
KeywordsReferralMedicineAnxietyCohortMental healthPsychiatryLogistic regressionPanic disorderCohort studyPanicFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

This study describes 6- and 12-month outcomes in a referral cohort with anxiety disorders and identifies treatment and prognostic factors related to these outcomes. Patients were recruited at three general hospital clinics, two psychologist-run clinics, and one psychiatric hospital clinic. Outcomes included severity of symptoms, physical and mental health status, and subjective global change in problem severity. Of 254 patients eligible for follow-up, 165 (65.0%) completed a follow-up questionnaire. Methods of treatment included consultation with return to the primary care physician (38.2%); or continued treatment at the clinic, with medications (16.4%), psychotherapy (22.4%), or both (23.0%). Both severity of symptoms and mental health status improved but remained abnormal at follow-up. In multiple logistic regression, subjective global improvement was related to a diagnosis of panic disorder only, treatment with psychotherapy, and type of referral. Change over time in symptom severity was related to clinic type, and change over time in mental health was related to clinic type and duration of previous treatment.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.283
Teacher spread0.273 · 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

Citations8
Published2000
Admission routes1
Has abstractyes

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Same venueThe Journal of Nervous and Mental DiseaseSame topicAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesFrench-language works237,207