Treating postoperative pain? Avoid tramadol, long-acting opioid analgesics and long-term use
Bibliographic record
Abstract
A recent cohort study investigated ‘the risk of transitioning from acute to prolonged use’ of opioid analgesics in patients undergoing elective surgery. Patients given tramadol or long-acting opioids after discharge were at greater risk of prolonged opioid use than those who were given other short-acting opioids. ### EBM verdict EBM Verdict on: Chronic use of tramadol after acute pain episode: cohort study. BMJ 2019 May 14. doi: 10.1136/bmj.l1849. Strong pain-relieving medicines called opioids are commonly prescribed when patients are discharged from hospitals. However, pain after elective surgery is usually short-lived. This cohort study1 addresses an important question regarding the prolonged use of opioid analgesics after elective surgery in light of the opioid crisis in the USA and Canada and increased prescribing of opioids in high-income countries.2 Tramadol is both a weak mu-opioid receptor agonist and a serotonin and norepinephrine reuptake inhibitor. Its active metabolite, O -desmethyltramadol, is longer acting than tramadol itself and is a more potent mu-opioid receptor agonist. Responses to tramadol, therefore, vary according to the genotype of the main metabolising enzyme, CYP2D6.3 Tramadol has been …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".