Opioid prescribing practices in trauma patients at discharge: An exploratory retrospective chart analysis
Bibliographic record
Abstract
This study examined the opioid prescribing patterns at discharge in the trauma center of a major Canadian hospital and compared them to the guidelines provided by the Illinois surgical quality improvement collaborative (ISQIC), a framework that has been recognized as being associated with reduced risk. This was a retrospective chart review of patient data from the trauma registry between January 1, 2018, and October 31, 2019. A total of 268 discharge charts of naïve opioid patients were included in the analysis. A Morphine Milligram Equivalents per day (MME/day) was computed for each patient who was prescribed opioids and compared with standard practice guidelines. About 75% of patients were prescribed opioids. More males (75%) than females (25%) were prescribed opioids to patients below 65 years old (91%). Best practice guidelines were followed in most cases. Only 16.6% of patients were prescribed over 50 mg MME/day, the majority (80.9%) were prescribed opioids for =<3 days and only 1% for >7 days. Only 7.5% were prescribed extended-release opioids and none were strong like fentanyl. Patients received a multimodal approach with alternatives to opioids in 88.9% of cases and 82.9% had a plan for opioid discontinuation. However, only 23.6% received an acute pain service referral. The majority of the prescriptions provided adhered to the best practice guidelines outlined by the ISQIC framework. These results are encouraging with respect to the feasibility of implementing opioid prescription guidelines effectively. However, routine monitoring is necessary to ensure that adherence is maintained.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".