A73 EFFECT OF FECAL IMMUNOCHEMICAL TEST CUT-OFF LEVELS ON ADENOMA DETECTION RATE: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Background Adenoma detection rate (ADR) is higher after a positive fecal immunochemical test (FIT) compared to direct screening colonoscopy. Purpose This meta-analysis evaluated how ADR, the rates of advanced adenoma detection (AADR), colorectal cancer detection (CDR), and sessile serrated lesion detection (SSLDR) are affected by different FIT positivity thresholds. Method We searched MEDLINE, EMBASE, CINAHL, and EBM Reviews databases for studies reporting ADR, AADR, CDR, and SSLDR according to different FIT cut-off values in asymptomatic average-risk individuals aged 50 to 74 years old. Data were stratified according to sex, age, time to colonoscopy, publication year, continent, and FIT kit type. Study quality, heterogeneity, and publication bias were assessed. Result(s) Overall, 4280 articles were retrieved and fifty-seven studies were included (332,281 FIT-positive colonoscopies; mean cecal intubation 96.2%; mean age 60.7 years; male 52.1%). Mean ADR was 55.9% (95% CI 53.2% – 58.6%), while mean AADR, CDR, and SSLDR were 27.2% (95% CI 24.3% – 30.1%), 5.4% (95% CI 4.7% – 6.1%), and 3.0% (95% CI 1.7% – 4.6%), respectively. For each 20 μg Hb/g increase in FIT cut-off level, ADR increased by 2.87% (95% CI 1.70% – 4.05%, p < 0.01), AADR increased by 3.90% (95% CI 2.76% – 5.05%, p < 0.01) and CDR by 1.46% (95% CI 0.66% – 2.24%, p < 0.01). Many detection rates were greater amongst males and Europeans. Conclusion(s) ADRs in FIT-positive colonoscopies are influenced by the adopted FIT positivity threshold, and identified targets, importantly, proved to be higher than most current societal recommendations. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared
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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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".