Green space exposure and colorectal cancer: A systematic review
Bibliographic record
Abstract
Green space has been linked to colorectal cancer, but the evidence is still limited and inconclusive. This review aimed to investigate the relationship between green space and CRC. The studies were searched using three primary journal databases: PubMed, Scopus, and Web of Science. The retrieved citations were screened, and data from articles about GS exposure and CRC were extracted. The Newcastle-Ottawa Quality Assessment Form for Cohort Studies was used to evaluate the studies' quality. Five of the 1792 articles identified were eligible for the final review, which included five cohort studies published between 2017 and 2022. Each one article from the United States, the United Kingdom, France, Belgium, and Germany and All studies are of high quality. Four studies reported CRC incidence and one study reported CRC mortality from GS exposure. There was no significant association between GS attributes (Normalized Difference Vegetation Index (NDVI), surrounding greenness, surrounding green area, proximity to GS (agricultural lands, urban GSs, and forests), and count of recreational facilities and parks) with CRC. Only one study discovered that a healthier ecosystem was linked to a lower CRC risk. Although the evidence is still limited, the findings may indicate the presence of other factors in the relationship between GS and CRC. Future research should continue to focus on the variation of GS and the factors that influence it. Specific attention to the development of GS has the potential to produce benefits while mitigating cancer risk.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".