Immuno-oncology Trial Endpoints: Capturing Clinically Meaningful Activity
Bibliographic record
Abstract
Immuno-oncology (I-O) has required a shift in the established paradigm of toxicity and response assessment in clinical research. The design and interpretation of cancer clinical trials has been primarily driven by conventional toxicity and efficacy patterns observed with chemotherapy and targeted agents, which are insufficient to fully inform clinical trial design and guide therapeutic decisions in I-O. Responses to immune-targeted agents follow nonlinear dose-response and dose-toxicity kinetics mandating the development of novel response evaluation criteria. Biomarker-driven surrogate endpoints may better capture the mechanism of action and biological response to I-O agents and could be incorporated prospectively in early-phase I-O clinical trials. While overall survival remains the gold standard for evaluation of clinical efficacy of I-O agents in late-phase clinical trials, exploration of potential novel surrogate endpoints such as objective response rate and milestone survival is to be encouraged. Patient-reported outcomes should also be assessed to help redefine endpoints for I-O clinical trials and drive more efficient drug development. This paper discusses endpoints used in I-O trials to date and potential optimal endpoints for future early- and late-phase clinical development of I-O therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.215 | 0.237 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".