Family history of cancer as a potential risk factor for colorectal cancer in EMRO countries: A Systematic Review and Meta- Analysis
Bibliographic record
Abstract
Abstract Purpose To investigated existing articles about the impact of positive family history of cancer on increased risk of colorectal cancer in EMRO countries Method PubMed, Scopus, and Web of science are selected as our databases. Newcastle-Ottawa (NCO) Checklist is used for quality assessment. Odds Ratio with 95% confidence interval was used to compare the effect of family history of cancer in case and control group Result Finally, 27 articles are carefully selected to be in our study. Our Meta-analysis showed a significantly positive association between positive family history of ANY Cancer or CRC on increased risk of CRC (OR = 1.76; 95% CI:1.27–2.42; P = 0.001, OR = 2.21; 95% CI:1.54–3.17; P < 0.001 respectively). Subgroup analysis revealed that positive family history of ANY cancer in First-Degree significantly increased the risk of CRC (OR = 2.12; 95% CI:1.65–2.73; P < 0.001). Positive family history of CRC in First-Degree relatives is also associated with increased risk of CRC (OR = 2.19; 95% CI:1.22–3.91; P = 0.008). Conclusion Our results show the importance of screening and early identification of patients with family history. Coordinating health care facilities and encouraging people to use screening methods for early detection and therefore better treatment can reduce mortality and financial costs for general public.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".