Does psychosocial intervention improve survival in cancer? A meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is growing evidence that positive psychosocial intervention improves the wellbeing of cancer patients. Two meta-analyses conducted to date confirmed a significant small-to-moderate effect on quality of life. Previous randomized trials reported that psychosocial intervention also improved survival. However, more recent trials failed to detect a difference in survival. A systematic review of randomized trials that have examined the effectiveness of psychosocial intervention in cancer patients in terms of survival prolongation was conducted. METHODS: Randomized trials published between 1966 and June 2002 were identified through the databases of MEDLINE, EMBASE, CancerLit, CINAHL, Cochrane Library and reference lists of relevant articles. Relevant data were abstracted. The results of randomized trials were pooled using meta-analyses to estimate the effect of treatment on overall survival at one and four years in all cancer patients and also in breast cancer patients with metastases. RESULTS: Eight trials, which involved a total of 1062 patients (all cancer histologies), were identified. One- and four-year overall survival rates were obtained from eight trials and six trials, respectively. There was no statistically significant difference in the overall survival rates at one and four years [P = 0.6; RR 0.94 (95% CI 0.72, 1.22)] and [P = 0.5; RR 0.93 (95% CI 0.77, 1.13)], respectively. Four trials examined 511 metastatic breast cancer patients. Again, there was no statistically significant difference in the overall survival rates at one and four years [P = 0.3; RR 0.87 (95% CI 0.67, 1.14)] and [P = 0.3; RR 0.91 (95% CI 0.76, 1.10)], respectively. CONCLUSIONS: Psychosocial intervention does not prolong survival in cancer. This meta-analysis can not rule out small effect sizes because of the small number of trials and small trial sizes.
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 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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.062 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".