Does Subclinical Malabsorption of Carbohydrates Prevent Colorectal Cancer? A Hypothesis
Bibliographic record
Abstract
The incidence of colorectal cancer (CRC) is high in the western world and low in Asia and Africa. Fibre and starch are thought to be important protective factors, with a strong inverse relationship between starch consumption and CRC incidence. Whether this is true in Asia, particularly, and Africa is debatable. Because rice is the most easily absorbed of carbohydrates, a mechanism whereby there is an increased starch load in the colon in the Asian population needs to be identified. One possible cause is subclinical malabsorption. This is linked to increased mucosal permeability and low gross domestic product (GDP) per capita, which reflects poor sanitation and water supplies with increased risk for small bowel bacterial overgrowth leading to mucosal cell damage. A potential cause of the dramatic rise in CRC incidence in Japan may relate to its equally dramatic increase in GDP per capita of 600% over 50 years. This correlation appears to be stronger than with other dietary factors including fruit, vegetables and meat. Worldwide, a close correlation exists among low GDP per capita, low CRC incidence and presumed subclinical malabsorption. All these factors combine to maintain a low incidence of CRC in poorly developed countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 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".