Systemic cancer therapy: achievements and challenges that lie ahead
Bibliographic record
Abstract
In the last half of the century, advances in the systemic therapy of cancer, including chemotherapy, hormonal therapy, targeted therapy, and immunotherapy have been responsible for improvements in cancer related mortality in developed countries even as the population continues to age. Although such advancements have yet to benefit all cancer types, systemic therapies have led to an improvement in overall survival in both the adjuvant and metastatic setting for many cancers. With the pressure to make therapies available as soon as possible, the side-effects of systemic therapies, in particular long-term side-effects are not very well characterized and understood. Increasingly, a number of cancer types are requiring long-term and even lifelong systemic therapy. This is true for both younger and older patients with cancer and has important implications for each subset. Younger patients have an overall greater expected life-span, and as a result may suffer a greater variety of treatment related complications in the long-term, whereas older patients may develop earlier side-effects as a result of their frailty. Because the incidence of cancer in the world will increase over the next several decades and there will be more people living with cancer, it is important to have an understanding of the potential side-effects of new systemic therapies. As an introductory article, in this review series, we begin by describing some of the major advances made in systemic cancer therapy along with some of their known side-effects and we also make an attempt to describe the future of systemic cancer therapy.
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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 | 0.002 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".