BioCanRx Summit for Cancer Immunotherapy 2022 Proceedings
Bibliographic record
Abstract
From 19 to 21 November 2022, BioCanRx held its first post-pandemic in-person Summit for Cancer Immunotherapy in Montreal, Canada. The meeting was well attended by patients, trainees, researchers, clinicians, and industry professionals, who came together to discuss the current state and future of biotherapeutics for cancer in Canada and beyond. Three plenaries, three keynote speakers, a lively debate, and panel discussions, together with poster sessions and a social event, made the event memorable and productive. The current state of cellular therapies, cellular engineering, clinical trials, and the role of the cancer microbiome were discussed in plenary session, and the patient voice was welcomed and present throughout the meeting, in large part due to the Learning Institute, a BioCanRx initiative to include patient partners in research. In this meeting review, we highlight the platform presentations, keynote speakers, debate combatants, panellists, and the patient perspective on the annual meeting.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.109 | 0.063 |
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".