Pain Management Strategies and Adverse Effects of Opioids in Patients with Neurotrauma with Acute and Chronic Pain
Bibliographic record
Abstract
Pain is prevalent and a major source of disability after a traumatic brain injury (TBI) and a spinal cord injury (SCI). With a view of reducing the pain burden in neurotrauma, this study aimed to describe the use of pain management strategies and the adverse effects of opioids in patients with TBI and SCI. We collected data at hospital discharge (T1) and at 3 months post-injury (T2). A total of 70 patients, including 49 with TBI and 21 with SCI, with a mean age of 56 years (±21.1, ±17.9) were included. Almost a third of participants with TBI (33%) and SCI (29%) had a moderate average pain intensity at T1, and most experienced mild average pain intensity at T2. At T1, 80% of participants used opioids, whereas at T2, 26% of participants with TBI and 53% of those with SCI did. The main co-analgesic used was acetaminophen, with 78% and 17% for participants with TBI and 81% and 40% for participants with SCI at T1 and T2. The most common non-pharmacological strategy in participants with TBI was rest at T1 (45%) and T2 (32%), and comfortable positioning in participants with SCI at both timepoints (81% and 53%). The two most frequent adverse effects of opioids in both populations at T1 and T2 were drowsiness (35% vs. 43%; 10% vs. 13%) and constipation (27% vs. 38%; 7% vs. 20%). Opioids remain the most widely used pain management strategy in neurotrauma. Promoting a judicious use of opioids, combined with other strategies, could help patients with neurotrauma achieve adequate and safe pain relief.
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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) | 0.000 | 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 teacher head, 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".