Use of Telehealth for Domiciliary Follow-up After Hematopoietic Cell Transplantation During the COVID-19 Pandemic: Prospective Pilot Study
Bibliographic record
Abstract
BACKGROUND: Patients who have recently received a hematopoietic cell transplant (HCT) are at higher risk of acute complications in the first weeks after discharge, especially during the COVID-19 pandemic. OBJECTIVE: The aim of this study was to test the use of a telehealth platform for the follow-up of HCT patients during the first two weeks after discharge. METHODS: In total, 21 patients who received autologous or allogeneic HCT for hematological malignancies were screened from April 30, 2020, to July 15, 2020. The telehealth platform assisted in the daily collection of vital signs as well as physical and psychological symptoms for two weeks after hospital discharge. The required medical devices (oximeter and blood pressure monitor) were given to patients and a dedicated smartphone app was developed to collect this data. The data were reviewed daily through web-based software by a hematologist specializing in HCT. RESULTS: Only 12 of 21 patients were able to join and complete the study. Technological barriers were the most frequent limiting factor in this study. Among the 12 patients who completed the study, adherence to data reporting was high. The patients' experience of using such a system was considered good. In two cases, the system enabled the early recognition of acute complications. CONCLUSIONS: This pilot study showed that telehealth systems can be applied in the early posttransplant setting, with evident advantages for physicians and patients for both medical and psychological aspects. Technological issues still represent a challenge for the applicability of such a system, especially for older adult patients. Easier-to-use technologies could help to expand the use of telehealth systems in this setting in the future.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".