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Record W2062433744 · doi:10.1001/jama.2008.667

Depression Screening and Patient Outcomes in Cardiovascular Care

2008· review· en· W2062433744 on OpenAlexaff
Brett D. Thombs, Peter de Jonge, James C. Coyne, Mary A. Whooley, Nancy Frasure‐Smith, Alex J. Mitchell, Marij Zuidersma, Chete Eze‐Nliam, Bruno B. Lima, Cheri Smith, Karl A. Soderlund, Roy C. Ziegelstein

Bibliographic record

VenueJAMA · 2008
Typereview
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversité de MontréalMontreal Heart InstituteJewish General HospitalMcGill University
FundersNational Center for Complementary and Integrative Health
KeywordsMedicineDepression (economics)PsycINFOCINAHLMEDLINERandomized controlled trialPlaceboPhysical therapyMeta-analysisInternal medicinePsychiatryAlternative medicinePsychological interventionPathology

Abstract

fetched live from OpenAlex

CONTEXT: Several practice guidelines recommend that depression be evaluated and treated in patients with cardiovascular disease, but the potential benefits of this are unclear. OBJECTIVE: To evaluate the potential benefits of depression screening in patients with cardiovascular disease by assessing (1) the accuracy of depression screening instruments; (2) the effect of depression treatment on depression and cardiac outcomes; and (3) the effect of screening on depression and cardiac outcomes in patients in cardiovascular care settings. DATA SOURCES: MEDLINE, PsycINFO, CINAHL, EMBASE, ISI, SCOPUS, and Cochrane databases from inception to May 1, 2008; manual journal searches; reference list reviews; and citation tracking of included articles. STUDY SELECTION: We included articles in any language about patients in cardiovascular care settings that (1) compared a screening instrument to a valid major depressive disorder criterion standard; (2) compared depression treatment with placebo or usual care in a randomized controlled trial; or (3) assessed the effect of screening on depression identification and treatment rates, depression, or cardiac outcomes. DATA EXTRACTION: Methodological characteristics and outcomes were extracted by 2 investigators. RESULTS: We identified 11 studies about screening accuracy, 6 depression treatment trials, but no studies that evaluated the effects of screening on depression or cardiovascular outcomes. In studies that tested depression screening instruments using a priori-defined cutoff scores, sensitivity ranged from 39% to 100% (median, 84%) and specificity ranged from 58% to 94% (median, 79%). Depression treatment with medication or cognitive behavioral therapy resulted in modest reductions in depressive symptoms (effect size, 0.20-0.38; r(2), 1%-4%). There was no evidence that depression treatment improved cardiac outcomes. Among patients with depression and history of myocardial infarction in the ENRICHD trial, there was no difference in event-free survival between participants treated with cognitive behavioral therapy supplemented by an antidepressant vs usual care (75.5% vs 74.7%, respectively). CONCLUSIONS: Depression treatment with medication or cognitive behavioral therapy in patients with cardiovascular disease is associated with modest improvement in depressive symptoms but no improvement in cardiac outcomes. No clinical trials have assessed whether screening for depression improves depressive symptoms or cardiac outcomes in patients with cardiovascular disease.

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)0.018
metaresearch head score (Gemma)0.108
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.108
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.006
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.039
GPT teacher head0.361
Teacher spread0.322 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations476
Published2008
Admission routes1
Has abstractyes

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