Substance use in kidney transplant candidates and its impact on access to kidney transplantation
Bibliographic record
Abstract
BACKGROUND: Due to the increasing public acceptance of substance use, it is important to understand the association between substance use and access to kidney transplant and its outcomes. Here, we assess the sociodemographic predictors of substance use and the association between substance use and KT access. METHODS: Predictors of substance use were examined using a multivariable-adjusted multinomial logistic regression. The association between current substance use (tobacco and drug) and time from referral to listing or receipt of a KT was examined using Cox proportional hazards models. RESULTS: Of 2346 patients, the prevalence of current substance use was 17%. Predictors of current tobacco use were younger age, male sex, Caucasian ethnicity, being unemployed, and unmarried. Predictors of current drug use were younger age, male sex, Caucasian ethnicity, a history of non-adherence, and a history of mental health disorder. Patients with tobacco use had a decreased likelihood of being cleared for KT (hazard ratio [HR]:0.83[0.70, 0.99]) and receiving a KT (HR:0.80 [0.66, 0.96]). No association was seen in this sample for patients with drug use (HR:0.88 [0.69, 1.11] for being cleared for KT and 0.88 [0.69, 1.14] for KT, respectively). CONCLUSIONS: Tobacco use was associated with a decreased likelihood of access to KT whereas there was no statistically significant difference in access to KT between patients with or without drug use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".