Opioid-related harm in a quaternary pediatric hospital: A 5-year review
Bibliographic record
Abstract
Background: Opioid therapy in pediatrics may be particularly prone to error, yet the incidence of opioid-related medication error and harm has not yet been described in the pediatric inpatient setting.Methods: We reviewed a prospectively compiled medication safety database from November 1, 2012 to October 31, 2017. Reports originated from voluntary reporting, hospital code events, naloxone administrations, and reports of unexpected experiences of patient pain. Time, location, error characteristics, drug, route, prescription, error phase, mechanisms, harm, and outcome were collected for all reports. Error and harm were classified by the National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) system.Results: Over 697 opioid medication safety reports were included during the study period. Opioids were administered at a rate of 79.26 administrations per 100 patient bed days, with morphine and hydromorphone administered at 62 versus 15 administrations per 100 bed days, respectively. Overall error rate was 0.94 errors per 1,000 patient days. Although the absolute rate of error reporting was greater for morphine (0.65 errors reported per 1,000 opioid administrations) than for hydromorphone, the adjusted incidence of harm was 0.211 per 1,000 hydromorphone administrations compared to 0.086 per 1,000 morphine administrations. 47 opioid errors resulted in harm, and administration errors (29) were almost twice as common as prescribing errors (15).Conclusions: We report and aim to establish a comparative reference point for incidence of opioid-related error and harm adjusted for both hospital bed days and total opioid administrations within the pediatric hospital inpatient setting based on the above findings.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".