Patient and Provider Gender and Kidney Transplant Referral in Canada: A Survey of Canadian Healthcare Providers
Bibliographic record
Abstract
BACKGROUND: Referral for kidney transplant (KT) is variable, with women often disadvantaged. This study aimed to better characterize Canadian transplant referral practices and identify potential differences by respondent and/or patient gender using surveys targeted at healthcare practitioners (HCPs) involved in KT. METHODS: Surveys consisting of 25 complex patient cases representing 7 themes were distributed to KT HCPs across Canada (March 3, 2022-April 27, 2022) using national nephrology/transplant society email registries. Respondents were asked whether they would refer the patient for transplant. Two identical surveys were created, differing only by gender/gender pronouns used in each case. Multivariable logistic regression was used to assess the association of respondent demographics and patient themes (including case gender) with the odds of transplant referral (overall and stratifying by respondent gender). RESULTS: Overall, the referral rate was 58.0% among 97 survey respondents (46.4% male). Case themes associated with a lower likelihood of referral included adherence concerns (adjusted odds ratio [aOR] 0.65; 95% confidence interval [CI], 0.45-0.94), medical complexity (aOR 0.57; 95% CI, 0.38-0.85), and perceived frailty (aOR 0.63; 95% CI, 0.47-0.84). Respondent gender was not associated with differences in KT referral (aOR 0.91; 95% CI, 0.65-1.26 for male versus female respondents) but modified the association of frailty (less referral for male than female respondents, P = 0.005) and medical complexity (less referral for female than male respondents, P = 0.009) with referral. There were no differences in referral rate by case gender ( P = 0.82). CONCLUSIONS: KT referral practices vary among Canadian HCPs. In this study, there were no differences in likelihood of transplant referral by candidate gender.
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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 | 0.001 | 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) | 0.000 | 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".