Opioid prescription patterns among urologists as compiled from within Medicare
Bibliographic record
Abstract
INTRODUCTION: We aimed to evaluate opioid prescribing patterns of urologists across the United States (U.S.) and the District of Columbia (D.C.) using publicly available data from Medicare Part D. Our secondary analysis was to identify any loco-regional trends that may exist within the U.S. METHODS: We queried publicly reported information from the Part D prescriber database, which is compiled from beneficiaries enrolled within the Medicare Part D prescription drug program. Only providers with the specialty description of urologist were included in this study. RESULTS: Between 2013 and 2017, a five-year average of 452 901 opioid claims by 9640 urologists - amounting to $5 357 114 USD and comprising 3.78% of all claims made - were identified. The state of Maine featured the highest percentage of opioid claims in relation to all claims (5.81%). West Virginia had the greatest average total opioid claims per provider (90), while Michigan featured the highest average proportion of opioid claims per provider (10.63%). The fewest opioid claims were processed within the Mid-Atlantic and New England regions. CONCLUSIONS: A multitude of factors likely contributes to variability between states. Urologists should be increasingly aware of their individual prescription tendencies and use available drug monitoring programs to reduce unnecessary prescriptions, all while providing more targeted and appropriate pain management.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".