Healthy eating interventions in adults living with and beyond colorectal cancer: a systematic review
Bibliographic record
Abstract
BACKGROUND: Rates of cancer survival are increasing, with more people living with and beyond cancer. Lifestyle recommendations for cancer survivors are based largely on extrapolation from cancer prevention recommendations. The present study aimed to systematically review the literature on randomised controlled trials (RCTs) focusing on healthy eating interventions in people with colorectal cancer (CRC). METHODS: A structured search of electronic databases was conducted in March 2018 using medical subject headings (MeSH) and text words related to CRC and diet. The results of the literature searches were uploaded to online software for data management. Titles and abstracts were screened based on the inclusion and exclusion criteria and data were extracted. Quality of data was assessed using the Cochrane Handbook. RESULTS: Seven studies were identified, including six RCTs and one RCT protocol, with a total of 2233 participants from six studies, of whom 1010 (45%) had CRC. Three studies assessed anthropometrics demonstrating participants who received dietary intervention had a greater reduction in measurements. Six studies assessed changes in dietary components; however, only one demonstrated an increase in dietary fibre. Two studies reported improvements in quality of life favouring dietary intervention groups. CONCLUSIONS: The quality of identified studies was variable, with limited evidence to support dietary intervention improving dietary intake in people living with or after CRC. Studies to date have not been based on robust study design that has combined all dietary interventions linked to CRC. As a result of the heterogeneity of the studies identified, it was difficult to draw strong conclusions.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".