Folate and Cancer Prevention: A New Medical Application of Folate Beyond Hyperhomocysteinemia and Neural Tube Defects
Bibliographic record
Abstract
Folate is an important cofactor in the transfer of one-carbon moieties and plays a key role in DNA synthesis, repair, and methylation. The role of folate has greatly evolved from the prevention of macrocytic anemia to the prevention of cardiovascular disease and neural tube defects. More recently, epidemiologic, animal, and clinical evidence suggests that folate may also play a role in cancer prevention. Two recently published large, prospective epidemiologic studies suggest that maintaining adequate levels of serum folate or moderately increasing folate intakes from dietary sources and vitamin supplements can significantly reduce the risk of pancreatic and breast cancer, respectively. This protective effect of folate appears to be operative in subjects at risk for developing these cancers, namely, male smokers for pancreatic cancer and women regularly consuming a moderate amount of alcohol for breast cancer. Because the expanding role of folate nutrition in cancer prevention has major public health implications, research is required to clearly elucidate the effect of folate on carcinogenesis.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".