Immuno-oncology Trial Endpoints: Capturing Clinically Meaningful Activity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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 it