The Sugar-Cancer Connection Revisited: Aren’t Obesity, Cardiovascular, Metabolic Disorders and Awareness The Main Problems?
Bibliographic record
Abstract
Blood sugar had been proposed a long time ago to promote cancer. Since then, several studies were undertaken to either disprove or confirm that potential causal relationship. Among all forms of sugars – e.g., glucose, carbohydrates, fructose, maltose –, none was shown to increase directly or specifically the risks of cancer. Moreover, a reduction of glucose intake failed to prevent or reduce tumor cell activity. This said, all cells including cancer cells need sugar or more specifically glucose as fuel for their intrinsic cellular metabolism. One thing is clear about sugar or glucose — eating a lot of it generally leads to overstimulation of insulin, insulin growth factor (IGF), and overweight problems. In other words, as of today, scientists have failed to confirm a direct link between sugar and cancer, although an indirect link was found between cancer and all sources of energy such as lipids, proteins, alcohol, and sugar or glucose — i.e., when steadily taken in excess, all forms of energy lead generally to insulin problems, obesity, and type II diabetes which are, in turn, conditions well-known to enhance the risks of several types of cancer. Beyond a lack of knowledge about food, it is maybe poor awareness levels that underlie unconscious behaviors leading to excessive eating and abnormal sugar consumption mainly in Occidental 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.018 |
| 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".