Thriving Beyond Treatment: First Annual Allogeneic Blood and Marrow Transplant Survivorship Day
Bibliographic record
Abstract
Topic Significance & Study Purpose/Background/Rationale The Hans Messner Allogeneic Blood and Marrow Transplant program at the Princess Margaret Cancer Center (PMCC) was established in 1977. Since its inception over 3831 transplants have been performed. Inspired by the survivorship event run by the City of Hope in California, Dr. Mattsson, the program director, aimed to recreate at PMCC. The goal was to honour patients, caregivers, and the multidisciplinary healthcare team. Of importance, Dr. Mattsson wanted the inpatient nursing team, who witness the complex and challenging phase of stem cell transplant, to see their impact and as well positive outcomes after transplant. Methods, Intervention, & Analysis In May of 2023 a planning committee was established, this included physicians, nurses, administrative assistants and an event coordinator. An outdoor venue was sought to accommodate our immunocompromised population. Invitations were sent to patients who had undergone a transplant at least one year prior, these were disseminated via the patient portal, clinic visits, and phone. Patients with known musical talents were personally approached by the planning committee. Refreshments were chosen with the Canadian Food Guide to Safe Eating, and reviewed by our Registered Dietician. Meetings continued bi-weekly from May to September 2023. The event was funded through various sponsors, as there was no cost to attendees. Findings & Interpretation The event took place on September 27 th 2023 at Black Creek Village in Toronto and was attended by 374 people. Speeches showcased a patient who celebrated her 35 th anniversary since transplant. As well as, a patient who participated in our allo at home program, another transplanted for sickle cell disease and one who had recently traveled to Germany to meet her unrelated donor. In addition, a patient discussed the challenges his sibling donor faced traveling to Canada. Performances included an opera singer patient who performed alongside one of our BMT physicians and a patient who played music with his quartette. Discussion & Implications In conclusion, the event was a success, patients found a powerful sense of community, with others who truly understood their journeys. Staff members were inspired, as they witnessed the resilience and vitality of life after transplant.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".