Bibliographic record
Abstract
Objective To comprehensively evaluate the risk factors of colorectal cancer in China and to provide the basis for prevention and control of colorectal cancer. Methods The published studies were searched in the CBM, CNKI, VIP, WanFang databases and PubMed, and other relevant journals were also hand-searched to identify all the relevant case-control studies conducted from Jane of 1985 to Nov. of 2012. After the data-quality of collected studies was assessed using Newcastle-Ottawa Scale(NOS) criteria, the homogeneity of the data was tested by RevMan5.1 software. Meta-analysis was applied to calculate the pooled odds ratio and 95% confidence intervals. Results Total 25 original articles were recruited into meta-analysis according to the selection criteria. The cumulative cases and controls were 6 646 and 9 957, respectively. The meta-analysis showed that the pooled odds ratio values of mild physical activity, drinking tea, milk and its products, onion and garlic foods, whole grains,vegetables and fruits were between 0 and 1; the pooled odds ratio values of smoking, passive smoking, poor emotional self-regulation capacity, fried or smoked and preserved foods, red meat, animal oil, fat, salty diet, medical history of hemorrhoids, or gallbladder disease, and family history of cancer were between 1 and 2; the pooled odds ratio values of trauma history, barbecue food, eating greasy, appendicitis history, gastrointestinal ulcer, chronic colitis, family history of cancer were between 2 and 5; the pooled odds ratio values of intestinal polyps, mucus bloody stools, chronic constipation and diarrhea were greater than 5.Conclusion The lifestyles, mental trauma, the cooking way, fatty food, medical history of intestinal conditions, and family history of cancers are positively correlated to the colorectal cancer; the mild physical activity, drinking tea, milk and its products, dietary fibers are protective factors.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".