Resistance Management for Cancer: Lessons from Farmers
Bibliographic record
Abstract
One of the main reasons we have not been able to cure cancers is that treatments select for drug-resistant cells. Pest managers face similar challenges with pesticides selecting for pesticide-resistant insects, resulting in similar mechanisms of resistance. Pest managers have developed 10 principles that could be translated to controlling cancers: (i) prevent onset, (ii) monitor continuously, (iii) identify thresholds below which there will be no intervention, (iv) change interventions in response to burden, (v) preferentially select nonchemical control methods, (vi) use target-specific drugs, (vii) use the lowest effective dose, (viii) reduce cross-resistance, (ix) evaluate success based on long-term management, and (x) forecast growth and response. These principles are general to all cancers and cancer drugs and so could be employed broadly to improve oncology. Here, we review the parallel difficulties in controlling drug resistance in pests and cancer cells. We show how the principles of resistance management in pests might be applied to cancer. Integrated pest management inspired the development of adaptive therapy in oncology to increase progression-free survival and quality of life in patients with cancers where cures are unlikely. These pest management principles have the potential to inform clinical trial design.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".