Opioids After Discharge in Pediatric Patients
Bibliographic record
Abstract
To the Editor We read with interest the study by George et al.1 reporting on the characteristics of >30,000 discharge prescriptions for opioids in pediatric patients from a tertiary care, academic center. The authors observed that large quantities were prescribed that might not completely be used. They pointed out that leftover opioids in households constitute a significant public health concern for potential accidental intoxications or illicit use. They concluded the need to determine the actual quantities of opioids used in this setting. We have performed such a study.2 We prospectively recruited 243 pediatric patients discharged with a morphine prescription from our center after surgery. Our study focused on morphine because other opioids are much less used at our center. Oxycodone, the most common drug in the study by George et al., is not available in liquid form in Canada. We contacted parents by telephone 3 days after discharge to follow-up on opioid use and the safety aspects of this medication at home (obtention, administration, storage, and disposal). We found that when morphine was prescribed to be taken on a regular basis, the parents filled the order in 95% of cases and administered the medication regularly 56% of the time. On the contrary, 76% of parents filled the prescription when morphine was prescribed as needed. Yet, most parents administered only 2 doses or less. In a subset of patients, <10% of prescribed doses were administered. A significant proportion of parents did not have a plan to dispose of the remaining morphine at the end of treatment. Our findings suggest that there is a need to re-evaluate the quantity of opioids prescribed at discharge after surgery. There is also a need to better inform parents on how to safely dispose of unused medication. Maxime Thibault, BPharm, MScDenis Lebel, BPharm, MSc, FCSHPChristina Nguyen, BPharm, MScDepartment of PharmacyCHU Sainte-JustineMontreal, Quebec, Canada[email protected]
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| 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".