Therapy’s Shadow: A Short History of the Study of Resistance to Cancer Chemotherapy
Bibliographic record
Abstract
This article traces the history of research on resistance to drug therapy in oncology using scientometric techniques and qualitative analysis. Using co-citation analysis, we generate maps to visualize subdomains in resistance research in two time periods, 1975-1990 and 1995-2010. These maps reveal two historical trends in resistance research: first, a shift in focus from generic mechanisms of resistance to chemotherapy to a focus on resistance to targeted therapies and molecular mechanisms of oncogenesis; and second, a movement away from an almost exclusive reliance on animal and cell models and toward the generation of knowledge about resistance through clinical trial work. A close reading of highly cited articles within each subdomain cluster reveals specific points of transition from one regime to the other, in particular the failure of several promising theories of resistance to be translated into clinical insights and the emergence of interest in resistance to a new generation of targeted agents such as imatinib and trastuzumab. We argue that the study of resistance in the oncology field has thus become more integrated with research into cancer therapy - rather than constituting it as a separate domain of study, as it has done in the past, contemporary research treats resistance as the flip side to treatment, as therapy's shadow.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".