Prescription Opioid Misuse in Older Adult Surgical Patients
Bibliographic record
Abstract
ABSTRACT: The United States and many other developed nations are in the midst of an opioid crisis, with consequent pressure on prescribers to limit opioid prescribing and reduce prescription opioid misuse. This review addresses prescription opioid misuse for older adult surgical populations. We outline the epidemiology and risk factors for persistent opioid use and misuse in older adults undergoing surgery. We also address screening tools and prescription opioid misuse prevention among vulnerable older adult surgical patients (e.g., older adults with a history of an opioid use disorder), followed by clinical management and patient education recommendations. A significant plurality of older adults engaged in prescription opioid misuse obtain opioid medication for misuse from health providers. Thus, nurses can play a critical role in identifying those older adults at a higher risk for misuse and deliver quality care while balancing the need for adequate pain management against the risk for prescription opioid misuse.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".