Review article: obesity and colorectal cancer
Bibliographic record
Abstract
Summary Background Obesity is a growing global public health problem. More than half the European and North American population is overweight or obese. Colon and rectum cancers are still the second leading cause of cancer death worldwide, and epidemiological data support an association between obesity and colorectal cancers (CRCs). Aim To review the literature on CRC epidemiology in obese subjects, assessing the effects of obesity, including childhood or maternal obesity, on CRC, diagnosis, management, and prognosis, and discussing targeted prophylactic measures. Method We searched PubMed for obesity/overweight/metabolic syndrome and CRC. Other key words included ‘staging’, ‘screening’, ‘treatment’, ‘weight loss’, ‘bariatric surgery’ and ‘chemotherapy’. Results In Europe, about 11% of CRCs are attributed to overweight and obesity. Epidemiological data suggest that obesity is associated with a 30%–70% increased risk of colon cancer in men, the association being less consistent in women. Visceral fat or abdominal obesity seems to be of greater concern than subcutaneous fat obesity, and any 1 kg/m2 increase in body mass index confers more risk (hazard ratio 1.03). Obesity might increase the likelihood of recurrence or mortality of the primary cancer and may affect initial management, including accurate staging. The risk maybe confounded by different factors, including lower adherence to organised CRC screening programmes. It is unclear whether bariatric surgery helps reduce rectal cancer risk. Conclusions Despite a growing body of evidence linking obesity to CRC, many questions remain unanswered, including whether we should screen patients with obesity earlier or propose prophylactic bariatric surgery for certain patients with obesity.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".