Prescription Drug Shortages: Impact on Neonatal Intensive Care
Bibliographic record
Abstract
BACKGROUND: Prescription drug shortages have increased significantly during the past two decades and also impact drugs used in critical care and pediatrics. OBJECTIVES: To analyze drug shortages affecting medications used in neonatal intensive care units (NICUs). METHODS: Drug shortage data for the top 100 NICU drugs were retrieved from the University of Utah Drug Information Service from 2001 to 2016. Data were analyzed focusing on drug class, formulation, reason for shortage, and shortage duration. RESULTS: Seventy-four of the top 100 NICU drugs were impacted by 227 shortages (10.3% of total shortages). Twenty-eight (12.3%) shortages were unresolved as of December 2016. Resolved shortages had a median duration of 8.8 months (interquartile range 3.6-21.3), and generic drugs were involved in 175 (87.9%). An alternative agent was available for 171 (85.8%) drugs but 120 (70.2%) of alternatives were also affected by shortages. Parenteral drugs were involved in 172 (86.4%) shortages, with longer durations than nonparenteral drugs (9.9 vs. 6.4 months, p = 0.022). The most common shortage reason was manufacturing problems (32.2%). CONCLUSIONS: Drug shortages affected many agents used in NICUs, which can have quality and safety implications for patient care, especially in extremely low birth weight infants. Neonatologists must be aware of current shortages and implement mitigation strategies to optimize patient care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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; both teacher heads agree on what is shown here.
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".