Fecal Immunochemical Test Positivity Thresholds: An International Survey of Population-Based Screening Programs
Bibliographic record
Abstract
BACKGROUND: The fecal immunochemical test for hemoglobin (FIT) is now a widely used non-invasive test in population-based organized screening programs for colorectal neoplasia. The positivity thresholds of tests currently in use are based on the fecal hemoglobin concentration (f-Hb), but the rationale for the adopted thresholds are not well documented. To understand current global usage of FIT in screening programs we conducted an international survey of the brands of FIT used, the f-Hb positivity threshold applied and the rationale for the choice. METHODS: All members of the World Endoscopy Organization CRC Screening Committee were invited to complete an eight-element initial electronic survey exploring the key aims. Responses were obtained from 63 individuals, representing 38 specific locations in 28 countries. A follow-up survey on technical issues was offered to the 38 locations, with replies from 17 sites in 13 countries. RESULTS: In-use quantitative FIT were provided by four main manufacturers; Minaris Medical (2 countries), Eiken Chemical Company/Polymedco (21), Alfresa Pharma (2) and Sentinel Diagnostics (4). Of the 38 screening sites, 15 used the threshold of 20 µg hemoglobin/g feces, while thresholds ranged between 8.5 and 120 ug/g in the remainder. Seven explanations were given for adopted FIT thresholds; maximizing the sensitivity for colorectal neoplasia (n = 23) was the most common followed by the availability of colonoscopy resources (n = 18). Predictive value, specificity, and cost effectiveness were less frequently reported as the rationale. Nine sites found it necessary to change the threshold that they had initially selected. CONCLUSIONS: This international survey has documented the wide range of FIT positivity thresholds that are in current use. Quantitative FITs enable programs to achieve the desired program outcomes within available resource constraints by adjusting the positivity threshold. This supports the need for enabling positivity threshold adjustment of emerging new screening tests based on novel predictive biomarkers, rather than providing inflexible test endpoints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".