Opioid Prescribing for Kidney Stone Formers Undergoing Stone Removal
Bibliographic record
Abstract
Background: Kidney stone formers may have episodes of severe pain and be at increased risk of narcotic use. Alabama has a high rate of opioid use. Within, we examine differences in opioid prescribing for kidney stone formers requiring stone removal procedures. Methods: A retrospective review was conducted from June 2013 to July 2019. Twenty-five patients with recurrent cystine stones were randomly matched by age, gender, and procedure to 25 recurrent non-cystine and 25 first-time non-cystine stone formers. Patients underwent ureteroscopic stone removal and percutaneous nephrolithotomy. Opioids prescribed were identified through medical record review and the prescription drug monitoring program (PDMP) database. Morphine milligram equivalents (MMEs) standardized opioid utilization. Results: Opioids prescribed at discharge significantly decreased (mean MME ± SD), 216.8 ± 125.9 for 2013 - 2016 and 124.2 ± 106.1 for 2017 - 2019 (P < 0.001) corresponding to implementation of an institutional opioid stewardship program. Opioids prescribed within 180 days of stone removal were similar amongst all three cohorts, mean 3,377.6 MME/patient. Over this 6-year time period, there was no difference in total amount of opioids prescribed for each cohort, mean 27,987.8 MME. The majority of prescriptions (56.4%) and MME prescribed (91.9 %) were from pain management and primary care providers. Conclusions: MME prescribed for stone removal in an environment of high utilization has not declined and is not influenced by stone disease complexity. An opioid stewardship program was associated with decreased opioids prescribed by the surgeons conducting stone removal but had a negligible overall influence. The latter is driven by other care providers. World J Nephrol Urol. 2022;11(1):10-17 doi: https://doi.org/10.14740/wjnu428
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".