Counterpoint: From animal models to prevention of colon cancer. Criteria for proceeding from preclinical studies and choice of models for prevention studies.
Bibliographic record
Abstract
Corpet and Pierre (D. E. Corpet and F. Pierre, Cancer Epidemiol. Biomark. Prev., 12: 391-400, 2003) have reviewed the prevention studies made with the azoxymethane rat and Min mouse colon cancer models, and have shown that many agents reduce the numbers of these experimental tumors. They suggest that agents with preventive activity with little or no toxicity should be evaluated in clinical intervention studies without delay. I think that the decision to proceed to a clinical trial is more complex, and involves an understanding of the safety of the agent and of the strength and consistency of the preclinical data. However, I am also impressed by the wide range of agents that have been found to affect the development of colon cancer in animals. This suggests that human colon cancer may be the consequence of many different dietary and lifestyle deficiencies, a view supported by the observation that normal mice develop colon cancer when fed diets deficient in several food components known to prevent tumors with the azoxymethane rat model [Newmark, H. L. et al., Carcinogenesis (Lond.), 22: 1871-1875, 2001]. There is a clear need to evaluate the preventive effects of additional combinations of these agents, identified perhaps from the Corpet and Pierre review (D. E. Corpet and F. Pierre, Cancer Epidemiol. Biomark. Prev., 12: 391-400, 2003), or from actual human high-risk diets. With diets that increase the risk of "spontaneous cancer" in hand, the stage would be set for assessing the most effective ways to reduce colon cancer risk, again first with animal studies, then clinical trials, and then perhaps population studies.
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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 | 0.124 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".