Opioid Utilization and Perception of Pain Control in Hospitalized Patients: A Cross‐Sectional Study of 11 Sites in 8 Countries
Bibliographic record
Abstract
BACKGROUND: Hospitalized patients are frequently treated with opioids for pain control, and receipt of opioids at hospital discharge may increase the risk of future chronic opioid use. OBJECTIVE: To compare inpatient analgesic prescribing patterns and patients' perception of pain control in the United States and non-US hospitals. DESIGN: Cross-sectional observational study. SETTING: Four hospitals in the US and seven in seven other countries. PARTICIPANTS: Medical inpatients reporting pain. MEASUREMENTS: Opioid analgesics dispensed during the first 24-36 hours of hospitalization and at discharge; assessments and beliefs about pain. RESULTS: We acquired completed surveys for 981 patients, 503 of 719 patients in the US and 478 of 590 patients in other countries. After adjusting for confounding factors, we found that more US patients were given opioids during their hospitalization compared with patients in other countries, regardless of whether they did or did not report taking opioids prior to admission (92% vs 70% and 71% vs 41%, respectively; P < .05), and similar trends were seen for opioids prescribed at discharge. Patient satisfaction, beliefs, and expectations about pain control differed between patients in the US and other sites. LIMITATIONS: Limited number of sites and patients/country. CONCLUSIONS: In the hospitals we sampled, our data suggest that physicians in the US may prescribe opioids more frequently during patients' hospitalizations and at discharge than their colleagues in other countries, and patients have different beliefs and expectations about pain control. Efforts to curb the opioid epidemic likely need to include addressing inpatient analgesic prescribing practices and patients' expectations regarding pain control.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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, 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".