Opioid Prescriptions for Older Adults Discharged After Inpatient Orthopedic Rehabilitation
Bibliographic record
Abstract
OBJECTIVE: To guide efforts in reducing discharge opioid prescribing, we aimed to investigate the rate of discharge with opioids for opioid-naïve older adults in inpatient orthopedic rehabilitation and to identify factors associated with this outcome. DESIGN: This is a single-center case-control study of opioid-naïve older adults participating in inpatient orthopedic rehabilitation, with patients grouped based on whether they received an opioid prescription upon discharge. Patient data were collected through the electronic health record. Logistic regression was used to assess for variables that were associated with discharge with an opioid prescription. RESULTS: Three hundred ninety-one patients were included, with 238 (60.9%) discharged with an opioid prescription. Factors predicting receiving an opioid prescription included longer acute care length of stay, admission for knee replacement, higher pain scores, higher opioid dose, a shorter rehab length of stay, and the absence of a dementia diagnosis. Rates were highest in patients aged 65-74 (77.17%) and lowest in patients aged 85+ (49.59%); however, age was not an independent predictor when all other factors were considered. CONCLUSIONS: A large proportion of older patients were discharged from inpatient orthopedic rehabilitation with an opioid prescription, which may be linked to intrinsic and extrinsic patient factors that influence opioid prescribing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".