Mind-Body Therapies for Cancer Patients Living with Depression, Anxiety or Insomnia (MIRACLE): A Systematic Review with Individual Participant Data Network Meta-Analysis
Bibliographic record
Abstract
Depression, anxiety, and insomnia are common in cancer patients. Mind-body therapies (MBTs) are promising forms of treatment for cancer patients living with depression, anxiety, and insomnia. The objective of this study is to assess the effectiveness and acceptability of MBTs in cancer patients living with depression, anxiety, or insomnia. EMBase, PubMed, Cinahl, PsychINFO, IndMED, CSI-NISCAIR, CNKI, Clinicaltrial.gov, ChiCTR, and CTRI will be searched until October 2020 for relevant studies. Randomized controlled studies in which MBTs were tested in a cancer population will be selected. The authors of the selected studies will be contacted to obtain individual participant data. The participants who reached a defined clinical threshold for depression, anxiety, or insomnia will be selected for the three sub-studies on depression, anxiety, and insomnia, respectively. Pairwise and network meta-analyses will be used to assess the changes in depression, anxiety, sleep quality, and completion rate. We will assess the effect of the treatment dose (number and frequency of interventions) on effectiveness. The results of this study will inform clinical decision-making for the treatment of psychological disturbances in cancer patients. If MBTs are found effective, they will potentially be recommended as treatments for cancer patients with psychological symptoms.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.023 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".