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Record W2980678952 · doi:10.1136/ebnurs-2019-103180

Cognitive behavioural therapy is not effective for depression in advanced cancer but could help in anxiety or other psychological symptoms

2019· letter· en· W2980678952 on OpenAlexaff
Marie‐Ève Caron, Dave A. Bergeron

Bibliographic record

VenueEvidence-Based Nursing · 2019
Typeletter
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsAnxietyDepression (economics)Psychological therapyClinical psychologyCognitive behaviour therapyCognitionPsychologyPsychotherapistCognitive therapyPsychiatry

Abstract

fetched live from OpenAlex

Commentary on : Serfaty M, King M, Nazareth I, et al . Manualised cognitive-behavioural therapy in treating depression in advanced cancer: the CanTalk RCT. Health Technol Assess 2019;23:1–106. Patients suffering from cancer must face the illness itself, but also a lot of adversity throughout it. Indeed, there are multiple effects and side effects of cancer and a notable side effect is depression. Depression can affect up to 40% of cancer patients.1 CBT is an empirically effective treatment for severe depression. Therefore, …

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)0.029
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.037
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0370.033
Insufficient payload (model declined to judge)0.0110.010

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 head0.088
GPT teacher head0.408
Teacher spread0.320 · 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
GenreCommentary

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

Citations1
Published2019
Admission routes1
Has abstractyes

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