Mitigating Risks Associated with Secondary Intravenous (Iv) Infusions: An Empirical Evaluation of a Technology-Based, A Practice-Based, And a Training-Based Intervention
Bibliographic record
Abstract
Multiple intravenous (IV) infusions are commonly used in the clinical setting to administer numerous fluids and medications to patients. Secondary infusion , also known as piggyback infusion , is a specific multiple IV infusion setup to deliver intermittent medications. Errors related to the setup and administration of secondary infusions have led to patient safety concerns[1, 2]. However, there is currently no study that specifically aims to empirically test the effects of interventions on the safety of secondary infusions in the clinical setting. The objective of this experimental study was to empirically evaluate interventions that may reduce errors during the administration of secondary infusions. Three mitigating strategies (a technology-based, a practice-based, and a training-based intervention) were tested. Forty critical care nurse participants performed secondary infusion tasks in a high-fidelity simulated clinical environment, with and without interventions. The types and frequency of errors were collected. The effects of the interventions on workflow and the reduction of secondary infusion errors were investigated.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".