Head-to-Head Diagnostic Test Accuracy Meta-analysis of Colonoscopy and Fecal Immunochemical Test in Detecting Advanced Colon Neoplasia
Bibliographic record
Abstract
Background: Studies on the use of fecal immunochemical test (FIT) in colorectal screening have long assumed perfect accuracy for colonoscopy. No study to date has directly compared the diagnostic accuracy of colonoscopy and FIT to detect advanced neoplasia (AN) in a head-to-head diagnostic accuracy meta-analysis. Methods: A comprehensive electronic search was performed for a head-to-head comparison of FIT and colonoscopy using a third acceptable reference standard in asymptomatic adults. Cochrane methodology was used to perform a head-to-head diagnostic test accuracy (DTA) meta-analysis. Quality assessment tool for diagnostic accuracy studies-2 (QUADAS-2) was used to assess the risk of bias in included studies. Results: Two studies met the eligibility criteria. Overall sensitivity and specificity were 98.5 (95% CI 96.3-100%) and 100% (99.9-100%) for colonoscopy and 16.4% (10.3-22.6%) and 95.4% (94.3-96.4%) for FIT. Colonoscopy was significantly better than FIT (P < 0.0001). The positive and negative likelihood ratios (LRs) were 1.75 (1.57-1.96) and 0.03 (0.01-0.08) for colonoscopy and 3.02 (2.01-4.55) and 0.88 (0.82-0.95) for FIT, respectively. Conclusion: Colonoscopy provides significantly better diagnostic accuracy to detect AN compared with FIT (GRADE: ⨁⨁◯◯). Our study provided precise sensitivity and specificity of both colonoscopy and FIT and a revision in screening policies based on an updated cost-effectiveness analysis considering the results of the head-to-head analysis.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".