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Record W1983117185 · doi:10.1371/journal.pone.0027181

Depression Screening and Patient Outcomes in Cancer: A Systematic Review

2011· review· en· W1983117185 on OpenAlexafffund
Anna Meijer, Michelle Roseman, Katherine Milette, James C. Coyne, Michael Stefanek, Roy C. Ziegelstein, Erin Arthurs, Allison Leavens, Steven C. Palmer, Donna E. Stewart, Peter de Jonge, Brett D. Thombs

Bibliographic record

VenuePLoS ONE · 2011
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity Health NetworkMcGill UniversityUniversity of TorontoJewish General Hospital
FundersCanadian Institutes of Health ResearchMedical Research CouncilJewish General HospitalUniversitair Medisch Centrum GroningenConcordia UniversityCanadian Breast Cancer Research AllianceMcGill UniversityRijksuniversiteit GroningenEli Lilly and Company
KeywordsMedicineCancer screeningMEDLINECancerDepression (economics)BiologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Several practice guidelines recommend screening for depression in cancer care, but no systematic reviews have examined whether there is evidence that depression screening benefits cancer patients. The objective was to evaluate the potential benefits of depression screening in cancer patients by assessing the (1) accuracy of depression screening tools; (2) effectiveness of depression treatment; and (3) effect of depression screening, either alone or in the context of comprehensive depression care, on depression outcomes. METHODS: Data sources were CINAHL, Cochrane, EMBASE, ISI, MEDLINE, PsycINFO and SCOPUS databases through January 24, 2011; manual journal searches; reference lists; citation tracking; trial registry reviews. Articles on cancer patients were included if they (1) compared a depression screening instrument to a valid criterion for major depressive disorder (MDD); (2) compared depression treatment with placebo or usual care in a randomized controlled trial (RCT); (3) assessed the effect of screening on depression outcomes in a RCT. RESULTS: There were 19 studies of screening accuracy, 1 MDD treatment RCT, but no RCTs that investigated effects of screening on depression outcomes. Screening accuracy studies generally had small sample sizes (median = 17 depression cases) and used exploratory methods to set sample-specific cutoff scores that varied substantially across studies. A nurse-delivered intervention for MDD reduced depressive symptoms moderately (effect size = 0.37). CONCLUSIONS: The one treatment study reviewed reported modest improvement in depressive symptoms, but no evidence was found on whether or not depression screening in cancer patients, either alone or in the context of optimal depression care, improves depression outcomes compared to usual care. Depression screening in cancer should be evaluated in a RCT in which all patients identified as depressed, either through screening or via physician recognition and referral in a control group, have access to comprehensive depression care.

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)0.009
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.007
Bibliometrics0.0070.009
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.134
GPT teacher head0.336
Teacher spread0.202 · 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 designSystematic review
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

Citations80
Published2011
Admission routes2
Has abstractyes

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