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Record W2160334739 · doi:10.1192/bjp.bp.106.027169

Effect of comorbid anxiety on treatment response and relapse risk in late-life depression: controlled study

2007· article· en· W2160334739 on OpenAlexaff
Carmen Andreescu, Eric J. Lenze, Mary Amanda Dew, Amy Begley, Benoit H. Mulsant, Alexandre Y. Dombrovski, Bruce G. Pollock, Jacqueline Stack, Mark D. Miller, Charles F. Reynolds

Bibliographic record

VenueThe British Journal of Psychiatry · 2007
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsBaycrest HospitalUniversity of Toronto
FundersNational Institute on Minority Health and Health DisparitiesNational Institute of Mental Health
KeywordsAnxietyPharmacotherapyDepression (economics)PlaceboAntidepressantPsychiatryAnxiety disorderPsychologyClinical psychologyInternal medicineMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Comorbid anxiety is common in depressive disorders in both middle and late life, and it affects response to antidepressant treatment. AIMS: To examine whether anxiety symptoms predict acute and maintenance (2 years) treatment response in late-life depression. METHOD: Data were drawn from a randomised double-blind study of pharmacotherapy and interpersonal psychotherapy for patients age 70 years and over with major depression. Anxiety symptoms were measured using the Brief Symptom Inventory. Survival analysis tested the effect of pre-treatment anxiety on response and recurrence. RESULTS: Patients with greater pretreatment anxiety took longer to respond to treatment and had higher rates of recurrence. Actuarial recurrence rates were 29% (pharmacotherapy, lower anxiety), 58% (pharmacotherapy, higher anxiety), 54% (placebo, lower anxiety) and 81% (placebo, higher anxiety). CONCLUSIONS: Improved identification and management of anxiety in late-life depression are needed to achieve response and stabilise recovery.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.007
GPT teacher head0.289
Teacher spread0.282 · 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 designNon-randomized trial
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

Citations197
Published2007
Admission routes1
Has abstractyes

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