Long-term opioid use after discharge from inpatient musculoskeletal rehabilitation
Bibliographic record
Abstract
OBJECTIVE: To determine: (i) the prevalence of opioid-naïve patients discharged on opioids from a musculoskeletal rehabilitation inpatient unit; (ii) the prevalence of opioid use 6 months after discharge; and (iii) the efficacy of the Opioid Risk Tool in identifying long-term opioid use. DESIGN: Prospective study. PARTICIPANTS: Sixty-four opioid-naïve patients who were exposed to opioids during admission and who were discharged on an opioid. METHODS: Potentially eligible patients' charts were reviewed. Participants were interviewed during admission to obtain the opioid risk score and contacted 6 months after discharge via a semi-structured telephone interview. RESULTS: Twenty-eight percent of opioid-naïve patients, who were discharged on opioids were still using opioids 6 months after discharge from rehabilitation. There was a trend for higher Opioid Risk Tool scores in those still using opioids than in individuals who were not using opioids at 6 months (p = 0.053). CONCLUSION: Patients who are prescribed opioids during a hospital admission should be screened for risk of opioid misuse. This data suggests that the Opioid Risk Tool could identify a patient's potential for becoming a long-term user of opioids.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".