The Hospital at Home Program: No Place Like Home
Bibliographic record
Abstract
BACKGROUND: The treatment of children with cancer is associated with significant burden for the entire family. Frequent clinic visits and extended hospital stays can negatively affect quality of life for children and their families. METHODS: Here, we describe the development of a Hospital at Home program (H@H) that delivers therapy to pediatric hematology, oncology, and blood and marrow transplant (bmt) patients in their homes. The services provided include short infusions of chemotherapy, supportive-care interventions, antibiotics, post-chemotherapy hydration, and teaching. RESULTS: From 2013 to 2015, the H@H program served 136 patients, making 1701 home visits, for patients mainly between the ages of 1 and 4 years. Referrals came from oncology in 82% of cases, from hematology in 11%, and from bmt in 7%. Since inception of the program, no adverse events have been reported. Family surveys suggested less disruption in daily routines and appreciation of specialized care by hematology and oncology nurses. Staff surveys highlighted a perceived benefit of H@H in contributing to early discharge of patients by supporting out-of-hospital monitoring and teaching. CONCLUSIONS: The development of a H@H program dedicated to the pediatric hematology, oncology, or bmt patient appears feasible. Our pilot program offers a potential contribution to improvement in patient quality of life and in cost-benefit for parents and the health care system.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".