Medications Reconciled at Discharge Versus Admission Among Inpatients at a Children’s Hospital
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Discharge prescription practices may contribute to medication overuse and polypharmacy. We aimed to estimate changes in the number and types of medications reported at inpatient discharge (versus admission) at a tertiary care pediatric hospital. METHODS: Electronic medication reconciliation data were extracted for inpatient admissions at The Hospital for Sick Children from January 1, 2016, to December 31, 2017 (n = 22 058). Relative changes in the number of medications and relative risks (RRs) of specific types and subclasses of medications at discharge (versus admission) were estimated overall and stratified by the following: sex, age group, diagnosis of a complex chronic condition, surgery, or ICU (PICU) admission. Micronutrient supplements, nonopioid analgesics, cathartics, laxatives, and antibiotics were excluded in primary analyses. RESULTS: Medication counts at discharge were 1.27-fold (95% confidence interval [CI]: 1.25-1.29) greater than admission. The change in medications at discharge (versus admission) was increased by younger age, absence of a complex chronic condition, surgery, PICU admission, and discharge from a surgical service. The most common drug subclasses at discharge were opioids (22% of discharges), proton pump inhibitors (18%), bronchodilators (10%), antiemetics (9%), and corticosteroids (9%). Postsurgical patients had higher RRs of opioid prescriptions at discharge (versus admission; RR: 13.3 [95% CI: 11.5-15.3]) compared with nonsurgical patients (RR: 2.38 [95% CI: 2.22-2.56]). CONCLUSIONS: Pediatric inpatients were discharged from the hospital with more medications than admission, frequently with drugs that may be discretionary rather than essential. The high frequency of opioid prescriptions in postsurgical patients is a priority target for educational and clinical decision support interventions.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".