Buprenorphine and cannabidiol co-administration reduces survival in a mouse model of orthopedic trauma
Bibliographic record
Abstract
Introduction: Analgesic selection following orthopedic trauma presents unique challenges due to potential drug interactions and physiological stress. The impact of different analgesic regimens - buprenorphine, cannabidiol (CBD), their combination, or vehicle - on survival was investigated in a murine model of tibial fracture. Methods: Eighty male C57BL/6 mice were randomly assigned to one of four group: (1) Buprenorphine (0.1 mg/kg, administered subcutaneously every 12 h for 3 days) plus cannabidiol (CBD, 100 mg/kg, administered intraperitoneally once daily for 7 days); (2) CBD only; (3) Buprenorphine + vehicle; or (4) Vehicle only. All animals also received carprofen (20 mg/kg, subcutaneously, once daily for 3 days). Survival was monitored over 7 days post-injury, and necropsies were performed to identify probable causes of death. Results: Following an orthopedic trauma, mice that received buprenorphine plus CBD exhibited significantly lower survival than those that received either treatment alone or vehicle only (p = 0.0049 and p = 0.02, respectively). No differences were noted between the other groups. Necropsy revealed gastrointestinal complications in most fatalities, while two deaths were linked to acute respiratory arrest post-injection. Discussion: These findings suggest that while buprenorphine and CBD are individually well-tolerated, their co-administration may increase the risk of adverse outcomes in murine orthopedic trauma models. Combining cannabinoids and opioids in translational research requires caution and emphasizes the need for mechanistic evaluation in preclinical models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".