Double faecal immunochemical testing in patients with symptoms suspicious of colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Faecal immunochemical test (FIT)-directed pathways based on a single test have been implemented for symptomatic patients. However, with a single test, the sensitivity is 87 per cent at 10 µg haemoglobin (Hb) per g faeces. This aims of this study were to define the diagnostic performance of a single FIT, compared with double FIT in symptomatic populations. METHODS: Two sequential prospective patient cohorts referred with symptoms from primary care were studied. Patients in cohort 1 were sent a single FIT, and those in cohort 2 received two tests in succession before investigation. All patients were investigated, regardless of having a positive or negative test (threshold 10 µg Hb per g). RESULTS: In cohort 1, 2260 patients completed one FIT and investigation. The sensitivity of single FIT was 84.1 (95 per cent c.i. 73.3 to 91.8) per cent for colorectal cancer and 67.4 (61.0 to 73.4) per cent for significant bowel pathology. In cohort 2, 3426 patients completed at least one FIT, and 2637 completed both FITs and investigation. The sensitivity of double FIT was 96.6 (90.4 to 99.3) per cent for colorectal cancer and 83.0 (77.4 to 87.8) per cent for significant bowel pathology. The second FIT resulted in a 50.0 per cent reduction in cancers missed by the first FIT, and 30.0 per cent for significant bowel pathology. Correlation between faecal Hb level was only modest (rs = 0.58), and 16.8 per cent of double tests were discordant, 11.4 per cent in patients with colorectal cancer and 18.3 per cent in those with significant bowel pathology. CONCLUSION: FIT in patients with high-risk symptoms twice in succession reduces missed significant colorectal pathology and has an acceptable workload impact.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".