Performance of the Fecal Immunochemical Test in Patients With a Family History of Colorectal Cancer
Bibliographic record
Abstract
OBJECTIVE: To assess the performance of a fecal immunochemical test (FIT) among participants of a population-based colorectal cancer (CRC) screening program with one or more first-degree relatives (FDR) with CRC. METHODS: Asymptomatic 50 to 74 years olds with a FDR diagnosed with CRC, enrolled in a colon screening program completed FIT (two samples, cut-off 20 µg Hemoglobin/gram feces) and underwent colonoscopy. FIT-interval CRCs were identified from the British Columbia cancer registry. Logistic regression analysis was used to identify variables associated with the detection of CRC and high-risk polyps (nonmalignant findings that required a 3-year surveillance colonoscopy) in those patients undergoing FIT and colonoscopy. RESULTS: Of the 1387 participants with a FDR with CRC, 1244 completed FIT with a positivity rate of 10.8%, 52 declined FIT but underwent colonoscopy and 90 declined screening. Seven CRCs were identified: six in patients with a positive FIT, one in a patient who only had colonoscopy. No CRCs were found in patients with a negative FIT. The positive and negative predictive values of FIT in the detection of CRC were 4.8% and 100%, respectively. On multivariate logistic regression, positive FIT, and not type of family history, was the only variable associated with detection of CRC or high-risk polyps. At 2-year follow-up, there was no FIT interval cancer detected in the study cohort. CONCLUSION: FIT is more strongly associated with high-risk findings on colonoscopy than type of family history. FIT may be an alternative screening strategy to colonoscopy in individuals with a single FDR with CRC.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".