66 Prescribing Patterns of Opioids and Adjunctive Analgesics for Patients with Burn Injuries
Bibliographic record
Abstract
Abstract Introduction Large quantities of analgesics are prescribed to control pain among patients with burn injuries and may lead to chronic use and dependency. This study aimed to determine whether patients are overprescribed analgesics at discharge and to identify factors that influence prescribing patterns. Methods A retrospective review of patient charts (n = 199) between July 1, 2015 - 2018 were reviewed from a registry at a single burn center. Opioid, neuropathic pain agent (NPAs), acetaminophen, and ibuprofen quantities given before and at discharge were compared. Linear mixed regression models were used to identify factors that increased the amount of analgesics prescribed among burn care providers. Results On average, patients were prescribed significantly more analgesics at discharge compared to what was consumed pre-discharge (p < 0.0001). Specifically, on average, providers did not overprescribe the daily dose of analgesics, but overprescribed the duration of pain medications required. For every increase in percent TBSA, 14 MEQ more opioids, 203 mg more NPAs, 843 mg more acetaminophen, and 126 mg more ibuprofen were prescribed (p < 0.05). Surgery was a predictor for higher opioid and NPA prescriptions (p = 0.03), while length of stay was associated with fewer NPAs prescribed (p = 0.04). Fewer ibuprofen were given to patients with a history of substance misuse (p = 0.01). Conclusions The quantity of analgesics prescribed at discharge varied widely and often prescribed for long durations of time. Standardized prescribing guidelines should be developed to optimize how analgesics are prescribed at discharge.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".