Racialized and Immigrant Status and the Pursuit of Living Donor Kidney Transplant - a Canadian Cohort Study
Bibliographic record
Abstract
Introduction: Both immigrant and racialized status may be associated with the pursuit of living donor kidney transplant (LDKT). Methods: This study was a secondary analysis of a convenience cross-sectional sample of patients with kidney failure in Toronto, obtained from our "Comprehensive Psychosocial Research Data System" research database. The exposures included racialized, immigrant, and combined immigrant and racialized status (White nonimmigrant, racialized nonimmigrant, White immigrant and racialized immigrant). Outcomes include the following: (i) having spoken about LDKT with others, (ii) having a potential living donor (LD) identified, (iii) having allowed others to share the need for LDKT, (iv) having directly asked a potential donor to be tested, and (v) accept a hypothetical LDKT offer. We assessed the association between exposure and outcomes using univariable, and multivariable binary or multinominal logistic regression (reference: White or White nonimmigrant participants). Results: Of the 498 participants, 281 (56%) were immigrants; 142 (28%) were African, Caribbean, and Black (ACB); 123 (25%) were Asian; and 233 (47%) were White. Compared to White nonimmigrants, racialized immigrants (relative risk ratio [RRR]: 2.98; 95% confidence interval [CI]: 1.76-5.03) and racialized nonimmigrants (RRR: 2.84; 95% CI: 1.22-6.65) were more likely not to have spoken about LDKT with others (vs. having spoken or planning to do so). Both racialized immigrant (odds ratio [OR]: 4.07; 95% CI: 2.50-6.34), racialized nonimmigrants (OR: 2.68; 95% CI: 1.31-5.51) and White immigrants (OR: 2.68; 95% CI: 1.43-5.05) were more likely not to have a potential LD identified. Conclusion: Both racialized and immigrant status are associated with less readiness to pursue LDKT. Supporting patients to communicate their need for LDKT may improve equitable access to LDKT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".