A274 IMPACT OF FIT CUT-OFFS VALUES ON MISSED COLORECTAL CANCER AND HIGH-RISK LESIONS
Bibliographic record
Abstract
Colorectal cancer (CRC) is the third most common cancer worldwide and carries a high mortality rate. Early screening has decreased morbidity and mortality from CRC. The fecal immunochemical test (FIT) uses antibodies against human hemoglobin and gives a quantitative value depending on the amount of hemoglobin detected in stool samples. The FIT is used in Canada as the standard for CRC screening of average risk populations. A lower numerical FIT is more sensitive for detecting “high-risk” lesions (HRL) but increases the number of colonoscopies that are performed; therefore, adjusting the positive value threshold allows screening programs to change the sensitivity and specificity of the test for detecting HRL and CRC 1. Determine the correlation between numerical FIT and high-risk colonoscopy findings 2. Determine the correlation between numerical FIT and different types of HRL Our study was a retrospective, population-based cohort study that identified patients aged 50–74 years old who underwent colonoscopy as part of the Edmonton SCOPE program, a regional CRC screening program for patients who were FIT positive. FIT positive was defined as a value ≥75 ng/mL. Demographic data, including age, sex, and family history of CRC as well as colonoscopy findings, including number of polyps, HRL, or CRC was collected. HRL were defined as: polyp size ≥1 cm, ≥3 polyps that were tubular or sessile serrated adenomas (SSA), villous pathology, or high-grade dysplasia (HGD). Numerical FIT was then correlated with CRC and HRL Between January-December 2017, a total of 2369 patients underwent colonoscopy for FIT positivity. Males comprised 60.6% of patients with a mean age of 60.6 ± 7.0 years. Multivariable analysis, adjusting for age, sex, and family history, revealed each increase of 50 ng/mL resulted in a 3% increase in CRC or HRL detected by colonoscopy. Increased numerical FIT correlated with increased CRC as well as HGD and larger polyps (≥2 cm) but not with large hyperplastic polyps, villous polyps, tubular adenomas, or SSAs. When modeling a cut-off of ≥75 ng/mL to ≥100 ng/mL there was a 7.3% (2.0–17.6, CI 95%) increase in CRC miss rates and a 17.9% (13.4–20.6, CI 95%) increase in HRL miss rates; this was even more significant when the cut-off was increased from ≥75 ng/mL to ≥175 ng/mL with an increase in miss rates to 20.0% (10.4–33.0, CI 95%) for CRC and 44.1% (40.8–47.4, CI 95%) for HRL [Table 1]. Number needed to scope decreased by an average of 2.8 scopes for each increase in 25 ng/L for CRC but decreased minimally for HRL Although increasing the FIT cut-off value decreases the total number of colonoscopies performed, it significantly increases the rate of missed CRC and HRL. Additionally, numerical FIT appears to correlate with CRC and specific types of HRL including HGD and large polyps. Table 1. Detection rates, miss rates, and number needed to scope (NNS) for colorectal cancers and high-risk lesions at various FIT cut-offs. None
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".