Paediatric harmful adverse drug events (PHADE)
Bibliographic record
Abstract
Abstract Background and Objectives It is well established that adverse drug events are frequent in paediatric hospital practice. The objective of this study is to systematically quantify and report the incidence of harmful adverse drug events across our institution and to identify predominant medications and error types. Methods We prospectively compiled a validated medication safety database for paediatric inpatients within our institution over a three-and-a-half-year period. All incidences of apparent patient harm relating to medication error were investigated and analyzed to determine veracity, severity of harm, phase of medication process, error type, causative medication, and contributory factors enabling each event. Results We identified 59 harmful adverse drug events, with an overall rate of 15.5 per 105 patient bed days. Most events occurred during administration (n = 27) and prescribing (n = 26) phases. Almost half of all harm (49%) was associated with opioids; a broad range of medication classes accounted for other harm. Harmful events occurred in 7.3 per 105 administrations of morphine and 13.3 per 105 administrations of hydromorphone. Wrong dose was the most frequently encountered error type. Conclusions This is the first study to quantify harmful adverse drug events in paediatric hospital practice. Our prospective analysis and compilation of harmful medication errors in paediatric hospital practice, reported with denominators of opioid administrations, and patient bed days, is a new standard for comparison in the long-discussed problem of paediatric harmful adverse drug events. By focusing on identified problematic drugs, error types, and contributory factors, we identify opportunities for interventions, error prevention and harm reduction.
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.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".