Colorectal cancer screening: foreign guidelines on fecal immunochemical test cut-off (review)
Bibliographic record
Abstract
Introduction. According to WHO data for 2020, colorectal cancer occupies first (highest) position (excluding non-melanoma skin cancer) in the top ranking list of the most frequent cancers in the Russian Federation. Colorectal cancer screening plays a pivotal role in early diagnosis and treatment. The best positive threshold values for hemoglobin concentration in a quantitative fecal immunochemical test (FIT) are postulated in a fraction of foreign recommendations. We reviewed the FIT cut-off values in those clinical guidelines. Materials and methods. The relevant publications were retrieved from PubMed and Google. The search horizon covered the last decade. Searches used the terms: «fecal immunochemical test» AND «screening» OR «cancer» AND «colorectal» OR «colon» OR «rectum", as well as other semantic and thematic forms. The recommendations appeared in last decade were reviewed. Results. This review summarizes the cut-off values for hemoglobin concentration in FIT, included in the clinical and laboratory guidelines developed in such regions as: Europe, Canada, USA, New Zealand. Many CRC screening programs use the FIT with a threshold setting for interpreting a positive test result. In practice, a wide range of options for threshold hemoglobin concentrations is used to interpret positive results of quantitative FIT. The FIT cut-off value is critically important to select the size of population for further examination depending on capacity of colonoscopy units. Discussion and conclusions. The foreign guidelines don’t establish single unified approach for FIT results interpretation, which would be an optimal fit for all imaginable practical situations in healthcare system. Improvement of screening techniques based on FIT would lead to the further steps on the way towards more effective and safe CRC diagnosis.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".