From Famine to Feast: Developing Early-Phase Combination Immunotherapy Trials Wisely
Bibliographic record
Abstract
Not until the turn of this century has immunotherapy become a fundamental component of cancer treatment. While monotherapy with immune modulators, such as immune checkpoint inhibitors, provides a subset of patients with durable clinical benefit and possible cure, combination therapy offers the potential for antitumor activity in a greater number of patients. The field of immunology has provided us with a plethora of potential molecules and pathways to target. This abundance makes it impractical to empirically test all possible combinations efficiently. We recommend that potential immunotherapy combinations be chosen based on sound rationale and available data to address the mechanisms of primary and acquired immune resistance. Novel trial designs may increase the proportion of patients receiving potentially efficacious treatments and, at the same time, better define the balance of clinical activity and safety. We believe that implementing a strategic approach in the early development of immunotherapy combinations will expedite the delivery of more effective therapies with improved safety and durable outcomes.
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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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.016 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".