Trends in the epidemiology of young-onset colorectal cancer: a worldwide systematic review
Bibliographic record
Abstract
BACKGROUND: Recent data suggest that the risk of young-onset colorectal cancer (yCRC), in adults less than 50 years of age, is increasing. To confirm findings and identify contemporary trends worldwide, we conducted a systematic review of studies examining population-level trends in yCRC epidemiology. METHODS: We searched MEDLINE (1946-2018), EMBASE (1974-2018), CINAHL (1982-2018), and Cochrane Database of Systematic Reviews (2005-2018) for studies that used an epidemiologic design, assessed trends in yCRC incidence or prevalence, and published in English. Extracted information included country, age cut-off for yCRC, and reported trends in incidence or prevalence (e.g. annual percent change [APC]). We pooled similarly reported trend estimates using random effects models. RESULTS: Our search yielded 8695 articles and after applying our inclusion criteria, we identified 40 studies from 12 countries across five continents. One study assessed yCRC prevalence trends reporting an APCp of + 2.6 and + 1.8 among 20-39 and 40-49 year olds, respectively. 39 studies assessed trends in yCRC incidence but with substantial variability in reporting. Meta-analysis of the most commonly reported trend estimate yielded a pooled overall APCi of + 1.33 (95% CI, 0.97 to 1.68; p < 0.0001) that is largely driven by findings from North America and Australia. Also contributing to these trends is the increasing risk of rectal cancer as among 14 studies assessing cancer site, nine showed an increased risk of rectal cancer in adults less than 50 years with APCi up to + 4.03 (p < 0.001). CONCLUSIONS: Our systematic review highlights increasing yCRC risk in North America and Australia driven by rising rectal cancers in younger adults over the past two decades.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".