State-level variation in opioid prescribing after knee arthroscopy among the opioid-naïve in the USA: 2015–2019
Bibliographic record
Abstract
OBJECTIVE: It has been established that most patients prescribed opioids after minor surgery have tablets left over, better understanding the variation in opioid prescribing and variation in dosage of the prescription could guide efforts to reduce prescribing. This study describes the state-level variation in opioid prescribing after a knee arthroscopy among opioid-naïve patients. DESIGN: Retrospective cohort study. SETTING: Commercial insurance claims data. PARTICIPANTS: 98 623 individual across the USA with commercial insurance who were opioid-naïve and had a knee arthroscopy between 2015 and 2019. EXPOSURE: Patients who filled an opioid prescription within 3 days of a knee arthroscopy. OUTCOME MEASURES: Opioid prescriptions were measured as a pharmacy claim for filling an opioid within 3 days of a knee arthroscopy. We measured the patient and state-level opioid prescribing rate, tablet count, morphine milligram equivalent dose per prescription and risk-adjusted predicted opioid quantity. RESULTS: Overall, 72% of patients filled an opioid prescription with a median tablet count of 40 and median morphine milligram equivalent of 250. Patients with an invasive procedure (27.9% vs 22.4%; p<0.001), higher education level (p<0.001) and fewer comorbidities (0.9 vs 1.2, p<0.001) had higher rates of opioid prescribing. The prescribing rate in the highest state, Nebraska (85%), was double the prescribing rate in the lowest state, South Dakota (40%). Comparing the casemix adjusted expected prescribing rate to the observed prescribing rate displayed that 18 states had observed prescribing rates that were higher than their expected prescribing rates. CONCLUSION: Wide variation in the likelihood of receiving a prescription, depending on state of residence, was observed. The dosages prescribed were high and have been associated with transition to long-term use. These findings suggest that there is substantial opportunity for the development of guidelines to reduce variability in opioid prescribing for this common ambulatory procedure.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".