Design and Conduct of Early Clinical Studies of Immunotherapy: Recommendations from the Task Force on Methodology for the Development of Innovative Cancer Therapies 2019 (MDICT)
Bibliographic record
Abstract
PURPOSE: To review key aspects of the design and conduct of early clinical trials (ECT) of immunotherapy agents. EXPERIMENTAL DESIGN: The Methodology for the Development of Innovative Cancer Therapies Task Force 2019 included experts from academia, nonprofit organizations, industry, and regulatory agencies. The review focus was on methodology for ECTs testing immune-oncology therapies (IO) used in combination with other IO or chemotherapy. RESULTS: Although early successes have been seen, the landscape continues to be very dynamic, and there are ongoing concerns regarding the capacity to test all new drugs and combinations in clinical trials. CONCLUSIONS: Optimization of drug development methodology is required, taking into account early, late, and lower grade intolerable toxicities, novel response patterns, as well as pharmacodynamic data.
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 | 0.596 | 0.497 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".