A systematic review on pediatric medication errors by parents or caregivers at home
Bibliographic record
Abstract
INTRODUCTION: Medication errors (MEs) are frequent and, in some cases, can lead to hospitalization, disability, increased healthcare costs or, even, death. Most of pediatric medications are administered by parents or caregivers at home. It is necessary to explore the MEs at home to improve pediatric patient safety. AREAS COVERED: This study aimed to review the current literature on the frequency of pediatric MEs by parents or caregivers at home, their associated factors, and pediatric ME reporting systems. Citable original articles of any type of study design or reviews published from 2013 to 2021 were searched in Medline, Scopus, Embase, and ScienceDirect databases. EXPERT OPINION: The available data about the frequency of pediatric MEs at home varied from 30% to 80%. Current research suggests the risk of making a ME in pediatric patients at home may depend on the characteristics of the caregiver and may increase if a prescription contains ≥3 drugs. Findings conclude that providing dosing tools more closely matched to prescribed dose volumes, recommending the use of syringes as a measurement tool, and educational intervention for caregivers could be useful to reduce MEs. Concerning the reporting systems for pediatric MEs in the outpatient setting, no information was found.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".