Bibliographic record
Abstract
Colitis-associated colorectal cancer (CA-CRC) is the cause of death in 10%-15% of inflammatory bowel disease (IBD) patients. CA-CRC results from the accumulation of mutations in intestinal epithelial cells and progresses through a well-characterized inflammation to dysplasia to carcinoma sequence. Quantitative estimates of overall CA-CRC risks are highly variable ranging from 2% to 40% depending on IBD severity, duration and location, with IBD duration being the most significant risk factor associated with CA-CRC development. Recently, studies have identified IBD patients with similar patterns of colonic inflammation, but that differ with respect to CA-CRC development, suggesting a role for additional non-inflammatory risk factors in CA-CRC development. One suggestion is that select IBD patients carry polymorphisms in various low penetrance disease susceptibility genes, which pre-dispose them to CA-CRC development, although these loci have proven difficult to identify in human genome-wide association studies. Mouse models of CA-CRC have provided a viable alternative for the discovery, validation and study of individual genes in CA-CRC pathology. In this review, we summarize the current CA-CRC literature with a strong focus on genetic pre-disposition and highlight an emerging role for mouse models in the search for CA-CRC risk alleles.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".