Adverse Events and Preventable Adverse Events in Children
Bibliographic record
Abstract
CONTEXT: Patient safety has been recognized as an important problem in health care. However, knowledge about adverse events and preventable adverse events in children is relatively limited. OBJECTIVE: To describe the incidence and types of adverse events and preventable adverse events in children. DESIGN: Analysis of pediatric hospitalizations in the Colorado and Utah Medical Practice Study, which involved a retrospective, 2-level (nurse and physician) medical record review of a population-based, representative sample of all pediatric hospital discharges. MAIN MEASURES: Adverse events were defined as an injury caused by medical management rather than disease processes that resulted in either prolonged hospitalization or disability at discharge. A preventable adverse event was defined as an avoidable adverse event based on currently available knowledge and accepted practices. PATIENTS: 3719 discharged hospital patients, 0-20 years old, and 7528 nonelderly (21-65 years old) discharged adult patients in Colorado and Utah. SETTING: All hospitals in Colorado and Utah. RESULTS: Adverse events occurred in 1% of pediatric hospitalizations in Colorado and Utah; 0.6% were preventable. Preventable adverse events rates were 0.53% in neonates and infants (0-0.99 years), 0.22% in children 1-12 years of age, and 0.95% in adolescents 13-20 years of age, compared with a rate of 1.50% in nonelderly adults. Of preventable adverse event types, birth related (32.2%) and diagnostic related (30.4%) events were the most common and were significantly more common than surgically related preventable adverse events (3.5%). CONCLUSIONS: These data suggest that approximately 70,000 children hospitalized in the United States experience an adverse event each year; 60% of these events may be preventable. The epidemiology of adverse events and preventable adverse events in children is different than in adults. To reduce the adverse events that occur in hospitalized children, research should focus on adolescent hospitalized patients, birth-related medical care, and diagnostics in pediatric medicine.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".