Faecal immunochemical tests versus guaiac faecal occult blood tests: what clinicians and colorectal cancer screening programme organisers need to know
Bibliographic record
Abstract
Although colorectal cancer (CRC) is a common cause of cancer-related death, it is fortunately amenable to screening with faecal tests for occult blood and endoscopic tests. Despite the evidence for the efficacy of guaiac-based faecal occult blood tests (gFOBT), they have not been popular with primary care providers in many jurisdictions, in part because of poor sensitivity for advanced colorectal neoplasms (advanced adenomas and CRC). In order to address this issue, high sensitivity gFOBT have been recommended, however, these tests are limited by a reduction in specificity compared with the traditional gFOBT. Where colonoscopy is available, some providers have opted to recommend screening colonoscopy to their patients instead of faecal testing, as they believe it to be a better test. Newer methods for detecting occult human blood in faeces have been developed. These tests, called faecal immunochemical tests (FIT), are immunoassays specific for human haemoglobin. FIT hold considerable promise over the traditional guaiac methods including improved analytical and clinical sensitivity for CRC, better detection of advanced adenomas, and greater screenee participation. In addition, the quantitative FIT are more flexible than gFOBT as a numerical result is reported, allowing customisation of the positivity threshold. When compared with endoscopy, FIT are less sensitive for the detection of advanced colorectal neoplasms when only one time testing is applied to a screening population; however, this is offset by improved participation in a programme of annual or biennial screens and a better safety profile. This review will describe how gFOBT and FIT work and will present the evidence that supports the use of FIT over gFOBT, including the cost-effectiveness of FIT relative to gFOBT. Finally, specific issues related to FIT implementation will be discussed, particularly with respect to organised CRC screening programmes.
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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 | 0.030 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.035 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".