Abstracts of the Cell Therapy Transplant Canada 2024 Annual Conference
Bibliographic record
Abstract
On behalf of Cell Therapy Transplant Canada (CTTC), we are pleased to present the Abstracts of the CTTC 2024 Annual Conference. The conference was held on 1-3 May 2024 in beautiful Victoria, British Columbia, at the Victoria Conference Centre, and attracted 293 in-person delegates and five virtual attendees. Several plenary sessions were held on topics such as gene therapy for hemoglobin disorders, optimizing donor selection, graft-versus-host disease (GvHD) strategies, collaborative care, survivorship, graft failure, and CAR-T therapy. Poster authors presented their work during a lively and engaging networking reception on Thursday, 2 May, and oral abstract authors were featured during the oral abstract session in the afternoon of Friday, 3 May 2024. Forty-nine (49) abstracts were selected for presentation as posters and six (6) as oral presentations. Abstracts were submitted within four categories: (1) Basic/Translational Sciences, (2) Clinical Trials/Observations, (3) Laboratory/Quality, and (4) Pharmacy/Nursing/Other Transplant Support. The top six (6) abstract authors were invited to give an oral presentation, and the top four (4) poster abstracts were selected to receive an award. All of these were marked as "Award Recipient" within the relevant category. Three abstracts were determined by the peer review panel to be inappropriate for this conference and were not invited to present at the conference, and two authors withdrew their abstract; therefore, five abstract numbers are missing from the list. We congratulate all the 2024 abstract presenters on their research and contributions to the field.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.240 | 0.075 |
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".