Dietary Habits and Colorectal Cancer Risk
Bibliographic record
Abstract
According to the American Cancer Society (ACS) (2014), colorectal cancer (CRC) is the "3rd leading cause of cancer related deaths in the United States"(p. 3). Researches have hypothesized how dietary habits could increase an individual's risk of developing CRC. Diets high in red meat for example, have long been thought to produce carcinogens through cooking and digestive processes. These carcinogens damage colon DNA, leading to neoplastic changes (Raskov, Pommergaard, Burcharth, & Rosenberg, 2014). On the other hand, fiber has been suggested as having a protective nature to individuals with CRC risk. Throughout this literature review a case involving 65-year-old female from North Dakota will be explored. She presented with a chief complaint of loss of appetite, abdominal cramps, constipation, and blood in her stool. After undergoing diagnostic evaluation, she was diagnosed with adenocarcinoma of the colon. Searching the electronic databases of Pub Med and CINA.BL, as well as reviewing cited references from obtained articles completed a literature review. All articles were published between 2009~2015 from varying countries including United States, Japan, Europe, and Canada. In the following sections 10 studies will explore how an individual's diet could impact their risk of CRC. In conclusion, studies have shown diets high in red meats have the ability to increase an individual's risk of CRC. While diets high in vegetables and fiber can have a protective nature against CRC. The clinical benefit of this review is providing patients with this information to help lower CRC risk especially in those who have genetic predisposition.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".