Smart pump use in pediatric patients
Bibliographic record
Abstract
Medications for pediatric patients are individualized based on patient weight. An error in weight measurement, weight transcription in a patient’s chart, or weight entry into software or equipment can result in prescription and medication administration errors.1 Determination of the appropriate weight for dosage calculations is not always feasible, especially in the unstable patient, or may be complicated by clinical conditions such as severe fluid overload and obesity. In addition, the equipment used for weight measurement is often not routinely calibrated, and the use of different units of measure increases the risk of errors. The use of certain technologies can help reduce medication errors in pediatric patients.2 Smart pumps “are designed to alert the user when there is a risk of an adverse drug interaction, or when the user sets the pump’s parameters outside of specified safety limits,”3 but their effect on the delivery of safe health care is limited.3,4 At our 450-bed mother–child center, we recently implemented 645 smart pumps (Infusomat, B. Braun, Melsungen AG, Germany) and 400 syringe pumps (Perfusor, B. Braun). We customized a drug library of 177 medications by standardizing drug concentrations and setting dosage limits. A total of 12 drug library subsets with specific limits were defined according to patient population (i.e., neonatology intensive care, pediatric intensive care, anesthesia, and obstetrics).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".