Gender differences in use of prescription narcotic medications among living kidney donors
Bibliographic record
Abstract
Prescription narcotic use among living kidney donors is not well described. Using a unique database that integrates national registry identifiers for living kidney donors (1987-2007) in the United States with billing claims from a private health insurer (2000-2007), we identified pharmacy fills for prescription narcotic medications in periods 1-4 and >4 yr post-donation and estimated relative likelihoods of post-donation narcotic use by Cox regression. We also compared narcotic fill rates and medication possession ratios (MPRs, defined as (days of medication supplied)/(days observed)), between donors and age- and sex-matched non-donors. Overall, rates of narcotic medication fills were 32.3 and 32.4 per 100 person-years in periods 1-4 and >4 yr post-donation. After age and race adjustment, women were approximately twice as likely as men to fill a narcotic prescription in years 1-4 (adjusted hazard ratio, aHR, 2.28; 95% confidence interval, CI, 1.86-2.79) and >4 yr (aHR 1.70; 95% CI 1.50-1.93). MPRs in donors were low (<2.5% days exposed), and lower than among age- and sex-matched non-donors. Prescription narcotic medication use is more common among women than men in the intermediate term after live kidney donation. Overall, total narcotic exposure is low, and lower than among non-donors from the general population.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".