Health Care Costs and Utilization in Patients Receiving Prescriptions for Long-acting Opioids for Acute Postsurgical Pain
Bibliographic record
Abstract
OBJECTIVES: Severe pain after joint replacement surgeries is common and is usually managed by opioid analgesics. We described joint replacement surgery patients who received prescriptions for long-acting opioids (LAOs) and compared their health care utilization and costs with postsurgical patients who did not receive LAO prescriptions. MATERIALS AND METHODS: Patients undergoing hip, knee, or shoulder replacement surgery between January 1, 2008 and December 31, 2011were included in the study and were classified by their exposure to LAOs. We estimated multivariate models to compare the groups' health care utilization and costs in the first 7 days and in the 1, 3, 6, and 12 months after surgery. RESULTS: Of 118,816 patients who met our inclusion criteria, 15,094 (13%) received LAO prescriptions in 30 days following surgery. LAO recipients were slightly younger and more likely than nonrecipients to have taken antibiotics, antidepressants, benzodiazepines, antihypertensives, sedatives, muscle relaxants, and short-acting opioids in the 60 days before surgery. LAO recipients were more likely to have had a hospitalization and an emergency department visit in the subsequent 1 week and in the next 1, 3, 6, and 12 months. Patients receiving LAO prescriptions incurred greater costs in the 1 week and in the 1, 3, 6, and 12 months following their surgeries compared with patients who did not receive LAO prescriptions. DISCUSSION: We found associations between patients who received prescriptions for LAOs and increased costs and utilization. Future studies should elucidate causal relationships between LAOs and increased resource use. Providers should consider alternative pain management strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".