Untangling Infusion Confusion: A Comparative Evaluation of Interventions in a Simulated Intensive Care Setting
Bibliographic record
Abstract
OBJECTIVES: Assess interventions' impact on preventing IV infusion identification and disconnection mix-ups. DESIGN: Experimental study with repeated measures design. SETTING: High fidelity simulated adult ICU. SUBJECTS: Forty critical care nurses. INTERVENTIONS: Participants had to correctly identify infusions and disconnect an infusion in four different conditions: baseline (current practice); line labels/organizers; smart pump; and light-linking system. MEASUREMENTS AND MAIN RESULTS: Participants identified infusions with significantly fewer errors when using line labels/organizers (0; 0%) than in the baseline (12; 7.7%) and smart pump conditions (10; 6.4%) (p < 0.01). The light-linking system did not significantly affect identification errors (5; 3.2%) compared with the other conditions. Participants were significantly faster identifying infusions when using line labels/organizers (0:31) than in the baseline (1:20), smart pump (1:29), and light-linking (1:22) conditions (p < 0.001). When disconnecting an infusion, there was no significant difference in errors between conditions, but participants were significantly slower when using the smart pump than all other conditions (p < 0.001). CONCLUSIONS: The results suggest that line labels/organizers may increase infusion identification accuracy and efficiency.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".