Optimising weight-loss interventions in cancer patients—A systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Excess weight has been associated with increased morbidity and a worse prognosis in adult patients with early-stage cancer. The optimal lifestyle interventions to optimize anthropometric measures amongst cancer patients and survivors remain inconsistent. OBJECTIVE: To conduct a systematic review and network meta-analysis (NMA) of randomized controlled trials (RCTs) comparing the effects of exercise and dietary interventions alone or in combination on anthropometric measures of adult cancer patients and survivors. METHODS: A systematic search of Medline, Embase and the Cochrane Trials Registry was performed. Outcomes of interest included changes in weight, body mass index (BMI), and waist circumference. Screening and data collection were performed by two reviewers. Bayesian NMAs were performed. RESULTS: Overall, 98 RCTs were included; 75 were incorporated in NMAs (n = 12,199). Groups of intervention strategies included: 3 exercise interventions, 8 dietary interventions, 7 combination interventions of diet and exercise and standard care. Median intervention duration was 26 weeks. NMA suggested that diet alone (mean difference [MD] -2.25kg, 95% CrI -3.43 to -0.91kg) and combination strategies (MD -2.52kg, 95% CrI -3.54 to -1.62kg) were associated with more weight loss compared to standard care. All dietary interventions achieved a similar magnitude of weight loss (MD range from -2.03kg to -2.52kg). Both diet alone and combination strategies demonstrated greater BMI reductions versus standard care, and each of diet alone, exercise alone and combination strategies demonstrated greater reductions in waist circumference than standard care. CONCLUSION: Diet and exercise alone or in combination are effective lifestyle interventions to improve anthropometric measures in cancer patients and survivors. All reputable diets appear to be similarly effective to achieve weight loss.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.009 | 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) | 0.004 | 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".