67 Identifying Risk Factors that Increase Analgesic Requirements at Discharge Among Patients with Burn Injuries
Bibliographic record
Abstract
Abstract Introduction Opioids and neuropathic pain agents (NPAs) like gabapentin and pregabalin are commonly prescribed in large doses to achieve adequate pain control among patients with burn injuries. This patient population is therefore at greater risk of becoming dependent and misusing analgesics following their injuries. Factors that increase the risk of chronic use of opioids or NPAs among this patient population has not yet been characterized. The purpose of this study was to identify factors that increase the amount of analgesics required by patients with acute burn injuries at the time of discharge. Methods Patient charts from July 1, 2015 - 2018 were reviewed retrospectively to determine opioid and neuropathic pain agent (NPAs) requirements 24 hours before discharge (n = 199). Regression models were performed to determine whether the following risk factors increased analgesic requirements at discharge: surgical intervention; age; gender; TBSA; history of psychiatric disorder; and history of substance misuse. Results Patients with a history of substance misuse or who were managed surgically required higher doses of opioids at discharge compared to those without a history of misuse or those who were managed conservatively (p = 0.01 and 0.02, respectively). Similarly, patients who had undergone surgery required more NPAs compared to those who did not have surgical debridement of their injuries (p < 0.001). For every percent increase in TBSA, patients required 14 mg more NPAs (p = 0.01). In contrast, older patients and those with a longer hospital stay required fewer amounts of NPAs before they were discharged from hospital. For every increase in years of age, patients required on average 7 mg less NPAs (p = 0.006), and for each additional day in a patient’s length of stay, patients required 6 mg less NPAs (p = 0.009). Conclusions Predictors of high analgesic requirements at discharge include patients with a history of substance misuse, those who underwent surgical debridement of their burn injuries, and patients with higher TBSA. Characterizing patient risk factors that increase analgesic requirements may help burn care providers tailor how much narcotics and NPAs to prescribe each patient 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".