Updates on radiotherapy-immunotherapy combinations: Proceedings of 8th Annual ImmunoRad Conference
Bibliographic record
Abstract
The annual ImmunoRad Conference has established itself as a recurrent occasion to explore the possibility of combining radiation therapy (RT) and immunotherapy (IT) for clinical cancer management. Bringing together a number of preclinical and clinical leaders in the fields of radiation oncology, immuno-oncology and IT, this annual event fosters indeed essential conversations and fruitful exchanges on how to address existing challenges to expand the therapeutic value of RT-IT combinations. The 8th edition of the ImmunoRad Conference, which has been held in October 2024 at the Weill Cornell Medical College of New York City, highlighted exciting preclinical and clinical advances at the interface between RT and IT, setting the stage for extra progress toward extended benefits for patients with an increasing variety of tumor types. Here, we critically summarize the lines of investigation that have been discussed at the occasion of the 8th Annual ImmunoRad Conference.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".