Thresholds adjustments and impact on clinical performance of three FIT assays in a colorectal cancer screening program
Bibliographic record
Abstract
OBJECTIVES: Fecal immunochemical tests (FITs) from various manufacturers yield different results, leading to variable positivity rates and clinical performance. These differences can influence the healthcare costs of a colorectal cancer screening program (CRC-SP), specifically when switching among FIT manufacturers. The analytical and clinical performance of three FITs was investigated to determine adjusted thresholds using positivity rate harmonization. METHODS: A cohort of 6,600 participants from the Quebec CRC-SP received collection kits from three different manufacturers to sample the same fresh stool. Participants with positive results were referred for a colonoscopy which was considered positive if advanced neoplasia (AN) was detected. Positivity rates were determined for each FIT at an unadjusted threshold. Adjusted thresholds were determined using a positivity rate harmonization strategy. Clinical performance was then evaluated for each supplier at unadjusted and adjusted thresholds. RESULTS: Among 5,513 participants who fulfilled the inclusion criteria, 327 underwent colonoscopy. The FIT positivity rates at unadjusted thresholds differed between manufacturers. The adjusted thresholds determined to yield the same positivity rate were different for each FIT. A total of 69 participants were diagnosed with AN. Significant differences in concordance, discordance, sensitivity, and specificity were observed when using the unadjusted threshold. After applying adjusted thresholds, differences in clinical performance between manufacturers were no longer statistically significant. CONCLUSIONS: Threshold adjustment using a positivity rate harmonization strategy render differences in clinical performance statistically nonsignificant and leads to a stable colonoscopy rate between manufacturers. CRC screening programs should determine adjusted thresholds when changing FIT suppliers or when using multiple assays.
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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 | 0.037 | 0.073 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".