Performance of the fecal immunochemical test for colorectal cancer and advanced neoplasia in individuals under age 50
Bibliographic record
Abstract
The increased demand for colonoscopy combined with increased incidence of colorectal cancer (CRC) among younger populations presents a need to determine FIT performance among individuals in this age group. We conducted a systematic review to assess test performance characteristics of FIT in detecting CRC and advanced neoplasia in younger age populations. A search through December 2022 identified published articles assessing the sensitivity and specificity of FIT for advanced neoplasia or CRC among populations under age 50. Following the search, 3 studies were included in the systematic review. Sensitivity to detect advanced neoplasia ranged from 0.19 to 0.36 and specificity between 0.94 and 0.97 and the overall sensitivity and specificity were 0.23 (0.17-0.30) and 0.96 (0.94-0.98), respectively. Two studies that assessed these metrics in multiple age categories found similar sensitivity and specificity across all age groups 30-49. Sensitivity and specificity to detect CRC was assessed in one study and found no significant differences by age groups. These results suggest that FIT performance may be lower for younger individuals compared to those typically screened for CRC. However, there were few studies available for analysis. Given increasing recommendations to expand screening in younger age groups, more research is needed to determine whether FIT is an adequate screening tool in this population.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.002 | 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".