Smart pump use in pediatric patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it