Colorectal cancer screening: Opportunities to improve uptake, outcomes, and disparities
Bibliographic record
Abstract
Colorectal cancer screening has become a standard of care in industrialized nations for those 50 to 75 years of age, along with selected high-risk populations. While colorectal cancer screening has been shown to reduce both the incidence and mortality of colorectal cancer, it is a complex multi-disciplinary process with a number of important steps that require optimization before tangible improvements in outcomes are possible. For both opportunistic and programmatic colorectal cancer screening, poor participant uptake remains an ongoing concern. Furthermore, current screening modalities (such as the guaiac based fecal occult blood test, fecal immunochemical test and colonoscopy) may be used or performed suboptimally, which can lead to missed neoplastic lesions and unnecessary endoscopic evaluations. The latter poses the risk of adverse events, such as perforation and post-polypectomy bleeding, as well as financial impacts to the healthcare system. Moreover, ongoing disparities in colorectal cancer screening persist among marginalized populations, including specific ethnic minorities (African Americans, Hispanics, Asians, Indigenous groups), immigrants, and those who are economically disenfranchised. Given this context, we aimed to review the current literature on these important areas pertaining to colorectal cancer screening, particularly focusing on the guaiac based fecal occult blood test, the fecal immunochemical test and colonoscopy.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".