What causes prescribing errors in children? Scoping review
Bibliographic record
Abstract
OBJECTIVES: (1) Systematically assemble, analyse and synthesise published evidence on causes of prescribing error in children. (2) Present results to a multidisciplinary group of paediatric prescribing stakeholders to validate findings and establish how causative factors lead to errors in practice. DESIGN: Scoping review using Arksey and O'Malley's framework, including stakeholder consultation; qualitative evidence synthesis. METHODS: We followed the six scoping review stages. (1) Research question-the research question was 'What is known about causes of prescribing error in children?' (2) Search strategy-we searched MEDLINE, EMBASE, CINAHL (from inception to February 2018), grey literature and reference lists of included studies. (3) Article selection-all published evidence contributing information on the causes of prescribing error in children was eligible for inclusion. We included review articles as secondary evidence to broaden understanding. (4) Charting data-results were collated in a custom data charting form. (5) Reporting results-we summarised article characteristics, extracted causal evidence and thematically synthesised findings. (6) Stakeholder consultation-results were presented to a multidisciplinary focus group of six prescribing stakeholders to establish validity, relevance and mechanisms by which causes lead to errors in practice. RESULTS: . Primary evidence clarifying causes was lacking. CONCLUSIONS: Specific factors complicate prescribing for children and increase risk of errors. Primary research is needed to confirm and elaborate these causes of error. In the meantime, this review uses existing evidence to make provisional paediatric-specific recommendations for policy, practice and education.
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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 | 0.027 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".