Evaluating the effectiveness of a training program to support nurses to administer cryopreserved hematopoietic stem cells by intravenous push method.
Bibliographic record
Abstract
A training program was developed to prepare registered nurses (RNs) at one cellular therapy centre to administer cryopreserved cells by intravenous (IV) push method. There are two main methods of infusion for dimethyl sulfoxide (DMSO) cryopreserved hematopoietic stem cell (HSC) products, gravity drip and IV push. Administering DMSO by either route can cause hypersensitivity reactions. Administration of HSCs by gravity drip is slower, resulting in fewer DMSO reactions. However, prolonged exposure of DMSO once the cells are thawed increases the risk of cellular damage. The faster IV push method reduces cell damage and decreases staff time. An environmental review within Canadian transplant centres found that in most adult centres, nurses administer by gravity drip, and when IV push is required, cryopreserved hematopoietic stem cells are administered by physicians. Our centre's method was IV push by a physician or nurse practitioner (NP). As transplant numbers grew, capacity to perform this skill needed to expand. To maintain the current benefits of the IV push method and increase capacity in a hematopoietic transplant program, the role of infusing stem cells by the IV push method was transitioned from the NPs and physicians to RNs. A successful training program utilizing simulation to support these oncology nurses in learning the new skill was developed and evaluated.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".