Nonopioid analgesic use in older patients admitted for orthopedic rehabilitation
Bibliographic record
Abstract
BACKGROUND: Multimodal analgesia (MMA) combines opioids with nonopioid analgesics (NOAs) to mitigate opioid-related adverse events and development of opioid use disorders. Although MMA has become the standard for orthopedic perioperative pain management, guidance is less clear for the approximately 15% of patients who go on to require inpatient orthopedic rehabilitation (IOR) postoperatively. The IOR population tends to be older and frailer and hence likely more vulnerable to adverse events. Little research has been done to shed light on how NOAs are used in this population. OBJECTIVE: To characterize NOA prescribing in older versus younger adults during IOR admissions and to determine predictors of NOA prescribing in an older IOR population. DESIGN: Retrospective case-control study. SETTING: Two IOR wards at an academic rehabilitation hospital in Toronto, Canada. PATIENTS: All patients aged ≥50 years admitted for an orthopedic indication between November 2019 and June 2021; the patients aged <65 group was included for comparative characterization of NOA prescribing versus older peers. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Medication use and adverse events, pain, and rehabilitation outcomes such as the Functional Independence Measure, discharge destination, and length of stay. RESULTS: A total of 643 patient encounters were included; 48.2% used NOA. Age (odds ratio [OR]: 0.97; confidence interval [CI]: 0.95-0.99, p < .001) and prior NOA use (OR: 3.15; CI: 2.0-4.9, p < .001) were associated with NOA prescribing. Other positively associated factors included body mass index, psychiatric history, elective surgery, and admission from a specific referring hospital. Adverse events between NOA users and nonusers were similar. CONCLUSIONS: NOA prescribing is heterogeneous and declines with age in IOR. This points to an opportunity to explore integrating NOA into opioid-sparing MMA protocols tailored to older IOR patients, along with further study of the safety and benefit of these regimens.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".