Mini Review: Gut Microbiome & Early Detection of Colorectal Cancer
Bibliographic record
Abstract
Colorectal cancer is the fourth most common diagnosed cancer in Canada, and the third and fourth leading cause of cancer-related deaths in men and women respectively. However, survival is often dependent on the stage of the cancer with stage I having 5-year survival rates as high as 92% while stage IV having a 5-year survival rate of only 11%. Hence, early detection has been key in improving colorectal cancer prognosis. Currently, colonoscopies, fecal immunochemical test (FIT), and guaiac fecal occult blood test (gFOBT) are used in current clinical settings as early detection and screening tools. There has been a growing interest in the gut microbiome, and the role of gut dysbiosis in the development of colorectal cancer. This includes Fusobacterium Nucleatum, Clostridium Symbiosum, and Bacteroides Fragilis. Recent studies have shown that identification of certain changes in the gut microbiota can be used to identify high-risk patients for developing colorectal cancer. Therefore, this mini review focuses on current evidence surrounding the role of gut microbiota and key microorganisms identified in colorectal cancer development. The review also discusses the limitations and gaps in the literature regarding the efficacy of using the gut microbiome as a screening tool for colorectal cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".