Bibliographic record
Abstract
There is increasing evidence to suggest that acetylsalicylic acid (ASA) and other nonsteroidal anti-inflammatory drugs (NSAIDs) reduce the risk of colorectal cancer. This observation is supported by animal studies that show fewer tumours per animal and fewer animals with tumours after administration of several different NSAIDs. Studies in humans consistently support this hypothesis. Intervention data from familial adenomatosis coli establish that the process of human colonic adenoma polyp formation is affected. Supportive evidence comes from 21 of 23 human studies - both case-control and cohort. The reduced risk has been found in men and women, for cancers of the colon and the rectum and for the use of both ASA and the other NSAIDs. Earlier detection of lesions as a result of drug-induced bleeding does not seem to account for these findings. The molecular mechanisms responsible for the chemopreventive action of this class of drugs is not completely established. Protection may affect several pathways, including cell cycle arrest and induction of apoptosis. Because of the consistency of epidemiological, clinical and experimental data, there is no need for further placebo trials. At the same time, there is a need to establish the dose, duration and frequency of use required for cancer-preventive activity.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".