Psychotherapy for advanced cancer patients: A meta-analysis of the quality of life and survival assessments
Bibliographic record
Abstract
Abstract Objectives A meta-analysis has explored the effect of psychotherapy on the quality of life (QOL) but has not explored the effect on advanced cancer patients’ survival, which is highly debated. Therefore, we consider the survival days and QOL as the primary outcomes in our analysis. Methods Eligible studies were collected from four databases (PubMed, Embase, Cochrane Library, and Web of Science) until February 20, 2021. The pooled effect sizes were presented as weighted mean difference (WMD) or relative risk (RR) with 95% confidence intervals (CIs). Publication bias was evaluated by Egger's test, and I2 statistics was used to assess the heterogeneity. Results Thirty-three studies were finally included, containing 2,159 patients in the psychotherapy group and 2,170 patients in the control group. McGill Quality of Life Questionnaire (MQOL) and European Organization for Research and Treatment of Cancer Quality of Life-C15-Palliative (EORTC-QLQ-C15-Pal) supported that QOL of the psychotherapy group was significantly higher than that of the control group, and WMD value was 0.42 (95% CI: 0.12–0.71) and 17.26 (95% CI: 11.08–23.44), respectively. No significant difference was observed between the two groups regarding to the survival time (WMD: 17.85, 95% CI: −8.79, 44.49, P = 0.189). Moreover, the levels of anxiety, depression, confusion, pain, and suffering were lowered in psychotherapy group (all P < 0.05). Significance of results Psychotherapy could improve the QOL of advanced cancer patients but not affect the survival time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.056 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".