E-Clinic: an innovative approach to complex symptom management for allogeneic blood and stem cell transplant patients.
Bibliographic record
Abstract
The allogeneic blood and stem cell program (ABSCP) at Princess Margaret Hospital, Toronto, performs 75 transplants annually. Many patients live greater than 100 kilometres from the centre and require frequent visits to the hospital for posttransplant care. The weekly travel to clinic, combined with complex symptom issues and the overwhelming desire to be cared for in their home community, is a major burden to patients and care providers. Our team of oncology health professionals, led by the nurse practitioner on service, sought to determine whether telehealth videoconferencing would be a viable option as a care delivery model to meet the complex needs of our remote patients and care partners. We introduced telehealth into the ambulatory clinic as a pilot project in early 2005. Patients were selected based upon symptoms, therapeutic plan and geographical remoteness. Patient progress was monitored with a goal of transitioning patients from posttransplant hospital-based care to partnered self-care in their home communities. The purpose of this article is to illustrate the ABSCP telehealth program development using a patient case study, and to detail the clinical process improvements and overall program successes that have led to the integration of telehealth into everyday clinical practice as a viable service delivery option for patient-centred symptom management and treatment compliance with a geographically remote patient population.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".