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Record W2805077099 · doi:10.5430/jnep.v8n11p11

Depression screening in cancer patients: A narrative review

2018· review· en· W2805077099 on OpenAlexvenueno aff
Gulfama Abid, Vahe Kehyayan, Jessie Johnson

Bibliographic record

VenueJournal of Nursing Education and Practice · 2018
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsDepression (economics)Narrative reviewCancerMedicinePsychiatryNarrativeQuality of life (healthcare)Intensive care medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

Depression is a significant and most common psychological symptom in cancer patients that causes a high risk of emotional and somatic consequences leading to poor quality of life. Despite the high prevalence of depression in cancer patients, the lack of screening and under-diagnosis of depression continues to be common. Numerous studies have shown that depression is a substantial complication in cancer patients that may lead to a variety of psychological and physical (somatic) symptoms. Presently, there are no depression screening practices at the Hamad Medical Corporation’s (HMC) National Center for Cancer Care and Research (NCCCR), which is the principle public healthcare provider in the State of Qatar. The aim of this narrative review is to explore depression in cancer and discuss the need for screening for depression and to recommend suggestions and implications for future practice and research.

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)0.007
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.155
GPT teacher head0.528
Teacher spread0.373 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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