Exploring Potential Gender-Based Disparities in Referral for Transplant, Activation on the Waitlist and Kidney Transplantation in a Canadian Cohort
Bibliographic record
Abstract
Introduction: In the United States, women are less likely to be referred, activated on the waitlist, or undergo kidney transplant (KT) than men; contemporary Canadian data regarding access to transplant for women are lacking. Methods: Among patients initiating dialysis in Nova Scotia (NS), Canada from 2010 to 2020, we examined the association of candidate gender with overall access to KT, including the following: (i) odds of transplant referral within 1 year of dialysis initiation, (ii) odds of activation on the transplant waitlist (if referred), and (iii) time-to-transplantation (if activated) using logistic regression or Cox proportional hazards models as appropriate. Results: Among 749 patients deemed potentially eligible for transplant, women had lower transplant rates than men (adjusted hazard ratio [aHR]: 0.53, 95% confidence interval [CI]: 0.36-0.78); this was amplified among patients aged >60 years (aHR: 0.25, 95% CI: 0.09-0.69). Compared with men, women had a lower adjusted odds of transplant referral (adjusted odds ratio [aOR]: 0.57, 95% CI: 0.35-0.93) by 1 year after dialysis initiation. Among those referred, women had lower odds of waitlist activation than men (aOR: 0.58, 95% CI: 0.30-1.11); and among those activated, women had lower hazard of KT (aHR: 0.74, 95% CI: 0.51-1.09), though these differences were not statistically significant. Women in NS experience lower overall access to transplant, including less referral, activation and KT compared with men. Conclusion: Gender-based barriers to any of (or in this case each of) referral, activation, or transplantation result in inequities in access; identification of disparities at these critical decision points is an important first step toward ensuring equal access for all.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".