Implementation and evaluation of a virtual long-term follow-up clinic for allogeneic stem cell transplant survivors using the RE-AIM framework.
Bibliographic record
Abstract
562 Background: Long-term follow-up (LTFU) care is essential for survivors of allogeneic hematopoietic stem cell transplant (allo-HCT). Travel distance to specialty centers can pose a significant barrier to accessing care. We implemented a virtual LTFU clinic at a tertiary cancer center to reduce these burdens and optimize service delivery. Methods: This initiative used the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, and Maintenance) to evaluate a virtual LTFU clinic for allo-HCT survivors. Eligible patients were ≥2 years post-transplant, clinically stable, off immunosuppression, and followed by a primary care provider. From October 21, 2024, to March 10, 2025, patients were screened and invited to transition to virtual care via MSTeams or phone. Requisitions for required bloodwork were emailed in advance, and results were integrated into the EMR and reviewed with patients during the visit. A Patient Flow Coordinator facilitated scheduling and preparation. Patient-reported experience measures were collected through REDCap surveys pre- and post-intervention. Results: Patients reported improved satisfaction and greater involvement in care. Virtual care eliminated perceived barriers and was particularly valuable for those living far from the hospital. Key results are summarized in the table. Of the 77 eligible patients who declined virtual care, common reasons included: pre-existing plans to be in Toronto, preference for in-person visits, or having concurrent medical appointments. Notably, 13 (28.9%) of those who declined expressed interest in participating in the virtual program in the following year. Conclusions: Virtual LTFU care is a feasible and effective model for allo-HCT survivors, particularly in settings where distance impedes access. In comparison to baseline level, patients reported improved satisfaction, a greater sense of involvement in care, and fewer logistical challenges. With proper infrastructure, communication, and EMR integration, virtual care can enhance accessibility, equity and quality in long-term transplant survivorship. Measure Value Patients screened 203 Eligible for virtual care 125 (61.6%) Completed virtual visit 48 (38.4% of eligible) Mode of visit: MS Teams 33 (68.8%) Mode of visit: Phone 15 (31.3%) Avg. patient distance to hospital 140.8 km (range: 4.7-1543 km) Reported no barriers (intervention) 100% Felt involved in care (median score) 5 Satisfied with time spent (median score) 5
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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 | 0.033 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".