Patient survival following renal transplantation in Indigenous populations: A systematic review
Bibliographic record
Abstract
BACKGROUND: Inequities in health care predispose Indigenous populations to poor health outcomes. The objective of this study was to examine patient survival and other post-transplant outcomes of kidney transplantation among Indigenous patients compared with non-Indigenous populations. METHODS: A systematic review of MEDLINE, EMBASE, and Google Scholar was undertaken from inception to September 30, 2019, using a computerized search. Publication descriptors and methodological and statistical details were extracted. Articles were assessed using the methodological index for non-randomized studies (MINORS) scale. RESULTS: Twelve studies were included. All studies were retrospective and published between 2004 and 2018. Mean Indigenous patient age was 40 (range: 8-76), while non-Indigenous was 41 (range: 6-74). Mean sample size for Indigenous populations was 398 (range: 24-1459), while for non-Indigenous patients was 1102 (range: 53-7555). Eight studies examined indigenous populations in Australia, two in Canada, one in the United States, and one in New Zealand. All studies were considered of high methodological quality and clinically homogenous. Results indicated that patient survival, graft survival, and delayed graft function were significantly reduced among Indigenous populations compared with non-Indigenous populations. CONCLUSIONS: Post-transplant outcomes among various Indigenous populations are significantly worse compared with non-Indigenous populations. The reasons for poor outcomes are likely multifactorial. Improved standardized reporting of transplant outcomes of Indigenous patients is necessary to better inform healthcare services and improve clinical outcomes.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".