High-alert medications for hospitalised paediatric patients – a two-step survey among paediatric clinical expert pharmacists in Germany
Bibliographic record
Abstract
Paediatric patients are more vulnerable to be harmed by medication errors compared to adults due to pharmacokinetic and pharmacodynamic changes in their development, individual dosing calculations, and manipulation of ready to-use products intended for adult patients. According to the Institute of Safe Medication Practices, there are some "drugs that bear a heightened risk of causing significant patient harm when they are used in error"; these drugs are called high-alert medications (HAM). The two-step survey among paediatric clinical expert pharmacists presented here aimed to compile a nation-wide HAM list. To provide detailed guidance, this survey followed a drugbased approach, resulting in specific potential drug related problems (DRPs) and associated recommendations for prevention. In contrast to this approach, in the first round of the survey two drug classes were included that both were rated as HAM (i.e.chemotherapy and parenteral nutrition). Twenty single drugs were identified as HAM, 65% of which were cardiovascular or neurological drugs. The paediatric expert pharmacists mentioned in total 216 potential DRPs; in particular, they identified potential administration-related problems (28% of all DRPs), dosing-related problems (26%), and drug-choice-related problems (18%, e.g.drug confusion and drug monitoring). Moreover, they suggested 275 potential interventions to address these DRPs. Two thirds of all interventions dealt with the preparation by the hospital pharmacy, standardisation of processes (e.g.labelling), and education or training. In conclusion, this survey provided a German paediatric high-alert medication list from a paediatric pharmacist point of view. Moreover, the experts mentioned for the first time specific potential DRPs and associated interventions to guide a local multidisciplinary approach for preventing medication-related harm in children.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".