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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".