The Phoenix Rises: The Rebirth of Cancer Immunotherapy
Bibliographic record
Abstract
The idea that the immune system can control cancer is not new. It was first theorized by Paul Ehrlich in 1909 when he suggested that cancers spontaneously arise at a high frequency in humans but are kept under control by the immune system. Over the course of a century, this idea has moved in and out of favor, and only recently has the true potential of the immune system to treat cancer been realized. Cancer immunotherapy relies on activating an individual's own immune system to eradicate established tumors, and, in contrast to conventional cancer therapies, induces a dynamic response that can result in long-lasting remission. Current immunotherapies against existing cancers include various approaches, ranging from stimulating immune effectors to counteracting immune suppressors. Although there have been patients with certain tumor types that have achieved extraordinary survival benefits, for the majority of cancer patients, immunotherapy remains relatively ineffective. As a result, new therapies and therapeutic regimens that combine various immunological agents are being described and assessed at a breathtaking pace. These new strategies have already been shown to significantly enhance antitumor immunity; however, they can also result in increased immune toxicities. To balance the harms and benefits of these therapies, some critical questions must be addressed, including which therapies or therapeutic combinations provide the most benefit and should continue being studied, and which individuals will benefit from each of these treatments.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.031 |
| Insufficient payload (model declined to judge) | 0.008 | 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".