Colorectal cancer screening with faecal testing, sigmoidoscopy or colonoscopy: a systematic review and network meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Evaluate effectiveness, harms and burdens of faecal blood testing, sigmoidoscopy and colonoscopy screening for colorectal cancer over 15 years. DESIGN: We performed an update of a Cochrane systematic review, and performed network meta-analysis comparing randomised trials evaluating colorectal cancer screening with guaiac faecal occult blood test (gFOBT) (annual, biennial), faecal immunochemical test (FIT) (annual, biennial), sigmoidoscopy (once-only) or colonoscopy (once-only) in a healthy population, aged 50-79 years. We conducted subgroup analysis on sex. Follow-up >5 years was required for analysis of colorectal cancer incidence and mortality. RESULTS: 12 randomised trials proved eligible. Compared with no-screening, we found high certainty evidence for sigmoidoscopy screening slightly reducing colorectal cancer incidence (relative risk (RR) 0.76; 95% confidence interval (CI 0.70 to 0.83) and mortality (RR 0.74; 95% CI 0.69 to 0.80), while gFOBT screening had little or no difference on colorectal cancer incidence, but slightly reduced colorectal cancer mortality (annual: RR 0.69; 95% CI 0.56 to 0.86, biennial: RR 0.88; 95% CI 0.82 to 0.93). No screening test reduced mortality nor incidence by more than six per 1000 screened over 15 years. Sigmoidoscopy had a greater effect in men, for both colorectal cancer incidence (women: RR 0.86; 95% CI 0.81 to 0.92, men: RR 0.75, 95% CI 0.71 to 0.79), and mortality (women: RR 0.85; 95% CI 0.71 to 0.96, men: RR 0.67; 95% CI 0.61 to 0.75) (moderate certainty). CONCLUSIONS: In a 15-year perspective, sigmoidoscopy reduces colorectal cancer incidence, while sigmoidoscopy, annual and biennial gFOBT all reduce colorectal cancer mortality. Sigmoidoscopy may reduce colorectal cancer incidence and mortality more in men than in women. PROSPERO REGISTRATION NUMBER: CRD42018093401.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".