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Record W2785228096 · doi:10.1002/pon.4648

Effects and moderators of psychosocial interventions on quality of life, and emotional and social function in patients with cancer: An individual patient data meta‐analysis of 22 RCTs

2018· review· en· W2785228096 on OpenAlexaff
Joeri Kalter, Irma M. Verdonck‐de Leeuw, Maike G. Sweegers, Neil K. Aaronson, Paul B. Jacobsen, Robert U. Newton, Kerry S. Courneya, Joanne F. Aitken, Jo Armes, Cecilia Arving, Liesbeth Boersma, Annemarie Braamse, Yvonne Brandberg, Suzanne K. Chambers, Joost Dekker, Kathleen Ell, Robert J. Ferguson, Marieke Gielissen, Bengt Glimelius, Martine M. Goedendorp, Kristi D. Graves, Sue P. Heiney, Robert Horne, Myra S. Hunter, Bodil Johansson, Merel Kimman, Hans Knoop, Karen Meneses, Laurel Northouse, Hester S. A. Oldenburg, Judith B. Prins, Josée Savard, Marc van Beurden, Sanne W. van den Berg, Johannes Brug, Laurien M. Buffart

Bibliographic record

VenuePsycho-Oncology · 2018
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversité LavalUniversity of Alberta
FundersKWF KankerbestrijdingNational Institute for Health and Care Research
KeywordsModerationPsychosocialPsychological interventionRandomized controlled trialMeta-analysisCoping (psychology)Clinical psychologyQuality of life (healthcare)MedicineClinical trialPsychologyPsychotherapistInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: This individual patient data (IPD) meta-analysis aimed to evaluate the effects of psychosocial interventions (PSI) on quality of life (QoL), emotional function (EF), and social function (SF) in patients with cancer, and to study moderator effects of demographic, clinical, personal, and intervention-related characteristics. METHODS: Relevant studies were identified via literature searches in 4 databases. We pooled IPD from 22 (n = 4217) of 61 eligible randomized controlled trials. Linear mixed-effect model analyses were used to study intervention effects on the post-intervention values of QoL, EF, and SF (z-scores), adjusting for baseline values, age, and cancer type. We studied moderator effects by testing interactions with the intervention for demographic, clinical, personal, and intervention-related characteristics, and conducted subsequent stratified analyses for significant moderator variables. RESULTS: PSI significantly improved QoL (β = 0.14,95%CI = 0.06;0.21), EF (β = 0.13,95%CI = 0.05;0.20), and SF (β = 0.10,95%CI = 0.03;0.18). Significant differences in effects of different types of PSI were found, with largest effects of psychotherapy. The effects of coping skills training were moderated by age, treatment type, and targeted interventions. Effects of psychotherapy on EF may be moderated by cancer type, but these analyses were based on 2 randomized controlled trials with small sample sizes of some cancer types. CONCLUSIONS: PSI significantly improved QoL, EF, and SF, with small overall effects. However, the effects differed by several demographic, clinical, personal, and intervention-related characteristics. Our study highlights the beneficial effects of coping skills training in patients treated with chemotherapy, the importance of targeted interventions, and the need of developing interventions tailored to the specific needs of elderly patients.

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)0.028
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.054
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0210.069
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.003
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 head0.183
GPT teacher head0.454
Teacher spread0.271 · 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.

Study designMeta-analysis
DomainMethods
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

Citations113
Published2018
Admission routes1
Has abstractyes

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