Multiple Intravenous Infusions Phase 2b: Laboratory Study.
Bibliographic record
Abstract
BACKGROUND: Administering multiple intravenous (IV) infusions to a single patient via infusion pump occurs routinely in health care, but there has been little empirical research examining the risks associated with this practice or ways to mitigate those risks. OBJECTIVES: To identify the risks associated with multiple IV infusions and assess the impact of interventions on nurses' ability to safely administer them. DATA SOURCES AND REVIEW METHODS: Forty nurses completed infusion-related tasks in a simulated adult intensive care unit, with and without interventions (i.e., repeated-measures design). RESULTS: Errors were observed in completing common tasks associated with the administration of multiple IV infusions, including the following (all values from baseline, which was current practice): setting up and programming multiple primary continuous IV infusions (e.g., 11.7% programming errors)identifying IV infusions (e.g., 7.7% line-tracing errors)managing dead volume (e.g., 96.0% flush rate errors following IV syringe dose administration)setting up a secondary intermittent IV infusion (e.g., 11.3% secondary clamp errors)administering an IV pump bolus (e.g., 11.5% programming errors)Of 10 interventions tested, 6 (1 practice, 3 technology, and 2 educational) significantly decreased or even eliminated errors compared to baseline. LIMITATIONS: The simulation of an adult intensive care unit at 1 hospital limited the ability to generalize results. The study results were representative of nurses who received training in the interventions but had little experience using them. The longitudinal effects of the interventions were not studied. CONCLUSIONS: Administering and managing multiple IV infusions is a complex and risk-prone activity. However, when a patient requires multiple IV infusions, targeted interventions can reduce identified risks. A combination of standardized practice, technology improvements, and targeted education is required.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 0.003 |
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".