Identifying Risk Factors That Increase Analgesic Requirements at Discharge Among Patients With Burn Injuries
Bibliographic record
Abstract
Patients with burn injuries require large doses of opioids and gabapentinoids to achieve pain control and are often discharged from hospital with similar amounts. This study aimed to identify patient risk factors that increase analgesic requirements among patients with burn injuries and to determine the relationship between opioid and gabapentinoid use. Patient charts from July 1, 2015 to 2018 were reviewed retrospectively to determine analgesic requirements 24 hours before discharge. Linear mixed regression models were performed to determine patient risk factors (age, gender, history of substance misuse, TBSA of burn, length of stay in hospital, history of psychiatric illness, or surgical treatment) that may increase analgesic requirements. This study found that patients with a history of substance misuse (P = .01) or who were managed surgically (P = .01) required higher doses of opioids at discharge. Similarly, patients who had undergone surgical debridement required more gabapentinoids (P < .001). For every percent increase in TBSA, patients also required 14 mg more gabapentinoids (P = .01). In contrast, older patients (P = .006) and those with a longer hospital stay (P = .009) required fewer amounts of gabapentinoids before discharge. By characterizing factors that increase analgesic requirements at discharge, burn care providers may have a stronger understanding of which patients are at greater risk of developing chronic opioid or gabapentinoid misuse. The quantity and duration of analgesics prescribed at discharge may then be tailored according to these patient specific risk factors.
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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 | 0.001 |
| 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".