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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".