S326 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
Introduction: Fecal Immunoglobulin Test (FIT) has been advocated as the first line of screening for colorectal cancer in several jurisdictions. Most studies have focused on colorectal cancer as the outcome of interest. Our goal was to quantify the diagnostic accuracy of different threshold of fecal immunochemical testing as compared to colonoscopy for detection of colonic advanced neoplasia and potential modifiers using proper Cochrane methodology. Methods: A comprehensive electronic search was performed for studies on FIT using colonoscopy as reference standard, to detect advanced neoplasia. Cochrane methodology was used to perform a diagnostic test accuracy 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 tumour, time from FIT to colonoscopy. Results: 24 studies were included with no evidence of publication-bias. 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. The sensitivity and specificity of FIT were 31 (26-36)% and 95 (94-96)%, respectively for distal lesions and 20 (13-27)% and 95 (94-96)%, respectively for proximal lesions (P=0.16). These numbers were 18 (3 - 33)% and 97 (96 - 98)%, respectively in those older than 50 as compared to 10 (1 - 19)% and 98 (97 - 99)%, respectively, for those under 50 (P=0.47). Meta-regression analysis did not show any significant predictability for location of the study (Asian versus non-Asian, P=0.06), inclusion of patients with unclear or high risk (P=0.14), time gap from FIT to colonoscopy (P=0.09) and risk of bias (high or unclear as compared to low) criteria in diagnostic accuracy of FIT (P=0.82). Conclusion: Sensitivity of FIT might have been overestimated in previous studies focusing on colorectal cancer as compared to advanced neoplasia 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 diagnostic odds ratio by increasing specificity but without any effect on sensitivity.Figure 1.: SROC of diagnostic accuracy of colonoscopy and different cut-offs of FIT.Table 1.: Diagnostic test accuracy of FIT for most used cut-off.
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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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.071 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".