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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".