Potential Modifiers and Different Cut-offs in Diagnostic Accuracy of Fecal Immunochemical Test in Detecting Advanced Colon Neoplasia: A Diagnostic Test Accuracy Meta-analysis
Bibliographic record
Abstract
Background: Fecal immunoglobulin test (FIT) has been advocated as the first line of screening for colorectal cancer (CRC) in several jurisdictions. Most studies have focused on CRC as the outcome of interest. Our goal was to quantify the diagnostic accuracy of different thresholds of FIT as compared with colonoscopy for detection of advanced colonic neoplasia and potential modifiers using proper Cochrane methodology. Methods: A comprehensive electronic search was performed for studies on FIT using colonoscopy as the reference standard to detect advanced neoplasia. Cochrane methodology was used to perform a diagnostic test accuracy (DTA) meta-analysis. Diagnostic accuracy of different cut-offs of FIT, including 25, 50, 75, 100, 150, and 200 ng/mL, were calculated separately. Meta-regression analysis was also performed to detect potential a priori modifiers, including age, location of the tumor, and time from FIT to colonoscopy. Results: Twenty-four studies were included with no evidence of publication bias. The sensitivity of FIT did not decrease with lowering the cut-off, although specificity increased in higher cut-offs. Commonly used cut-offs of 50 ng/mL, 75 ng/mL, and 100 ng/mL for FIT provided sensitivity of 39%, 36%, 27% and specificity of 92%, 94%, 96%, respectively. Diagnostic accuracy of FIT did not significantly differ in proximal versus distal lesions or in individuals below or over the age of 50 years. The results remained robust in a meta-regression of the location of the study, time from FIT to colonoscopy, and methodological quality. Conclusion: The sensitivity of FIT might have been overestimated in previous studies focusing on CRC, and it seems to be independent of age, location of neoplasia, or cut-offs, contrary to some previous studies. Lowering the cut-off will reduce the diagnostic odds ratio (DOR) by increasing specificity but without any effect on sensitivity.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".