4th Summer School in Immuno-Oncology, July 1st–3rd, 2021, Athens, Greece
Bibliographic record
Abstract
The 4th Summer School in Immuno-Oncology was held from July 1st-July 3rd as a web meeting. Many eminent researchers and leading oncologists from Europe and the USA working on basic, translational and clinical cancer research participated, presented, and discussed the most recent advances in cancer immunology and immunotherapy. Besides sharing the newest information in the field of cancer immunology and immunotherapy, the meeting also focused on the actual translation of new knowledge acquired in the lab to the clinical setting; particular emphasis was given to the mode of action of novel therapeutic modalities and to biomarkers helpful for treatment decision-making, as well as to means that may improve cancer immunotherapeutic protocols used for the treatment of a variety of malignancies. The main topics presented by the speakers included: (1) mechanisms of tumor immune evasion and resistance; (2) host-tumor interactions and means to regulate antitumor immunity; (3) exploitation of new biomarkers and tumor or immune signatures able to potentially guide therapeutic interventions; (4) emerging therapeutic modalities for cancer treatment and specific immunotherapeutics for thoracic, genito-urinary, gastrointestinal, skin and breast cancers; and (5) innovative treatment options and alternatives to minimize the toxic adverse events of cancer immunotherapy.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.325 | 0.150 |
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".