National and subnational incidence, mortality and associated factors of colorectal cancer in China: A systematic analysis and modelling study
Bibliographic record
Abstract
Background: Due to their known variation by geography and economic development, we aimed to evaluate the incidence and mortality of colorectal cancer (CRC) in China over the past decades and identify factors associated with CRC among the Chinese population to provide targeted information on disease prevention. Methods: We conducted a systemic review and meta-analysis of epidemiolocal studies on the incidence, mortality, and associated factors of CRC among the Chinese population, extracting and synthesising data from eligible studies retrieved from seven global and Chinese databases. We pooled age-standardised incidence rates (ASIRs) and mortality rates (ASMRs) for each province, subregion, and the whole of China, and applied a joinpoint regression model and annual per cent changes (APCs) to estimate the trends of CRC incidence and mortality. We conducted random-effects meta-analyses to assess the effect estimates of identified associated risk factors. Results: = 0.42), while the ASMR of CRC decreased from 12.00 to 7.95 (per 100 000 person-years) between 1974 and 2020 with a slight downward trend (APC = -0.89). We analysed 62 risk factors with synthesized data; 16 belonging to the categories of anthropometrics factors, lifestyle factors, dietary factors, personal histories and mental health conditions were graded to be associated with CRC risk among the Chinese population in the meta-analysis limited to the high-quality studies. Conclusions: We found substantial variation of CRC burden across regions and provinces of China and identified several associated risk factors for CRC, which could help to guide the formulation of targeted disease prevention and control strategies. Registration: PROSPERO: CRD42022346558.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".