Rationale and design of APOLLO: a personalized rehAbilitation PrOgram in aLLOgeneic bone marrow transplantation
Bibliographic record
Abstract
BACKGROUND: Hematopoietic stem cell transplantation (HSCT) is a common therapy for many hematologic malignancies. While advances in transplant practice have improved cancer-specific outcomes, multiple and debilitating long term physical and psychologic effects remain. Patients undergoing allogeneic bone marrow transplantation (allo-BMT) are often critically ill at initial diagnosis and with necessary sequential treatments become increasingly frail and deconditioned. Despite modern treatment regimens and support, cardiovascular disease remains a leading cause of non-relapse mortality among allo-BMT survivors. Well-established multi-disciplinary care models such as cardiac rehabilitation offer holistic care including exercise training, nursing support, physical/occupational therapy, psychosocial support and nutritional education. HSCT patients may be excluded from conventional outpatient physical rehabilitation programs due to prolonged pancytopenia and frequent hospital admissions. In Canada, dedicated cancer-specific rehabilitation programs are available only at major tertiary academic centers. METHODS: The primary aim of this study will evaluate the feasibility and acceptability of a multimodal care navigation (nursing, exercise, nutrition) intervention with content delivery facilitated by a supportive care web-based 'app' extending from diagnosis to 1 year in the allogeneic bone marrow transplant population. Adult patients scheduled for allo-BMT will receive support from exercise specialist, nursing support and dietician expertise alongside a supportive care 'app' with additional in-person or virtual cardiac rehabilitation support. DISCUSSION: To our knowledge, no research team is taking such a holistic, multidisciplinary approach to address the debilitating physiologic and psychological consequences of allo-BMT. We expect the findings to inform the optimal timing and patient preferences to develop studies examining risk-specific, individualized interventions (including exercise, pharmacotherapy, combination treatments) to reduce or prevent symptoms and dysfunction. We expect this innovative program to identify ways to benefit innumerable patients with hematologic and other malignancies. Ultimately, we hope to transform supportive care in hematopoietic stem cell transplantation. TRIAL REGISTRATION: Clinicaltrials.gov ID: NCT05579678.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".