Liver Transplant Outcomes in a Canadian First Nations Population
Bibliographic record
Abstract
BACKGROUND: A higher incidence of autoimmune disorders may predispose First Nations (FN) individuals to higher rates and more severe episodes of rejection, graft loss and mortality following liver transplantation for advanced liver disease. METHODS: A retrospective review of patient outcomes in a single centre providing long-term follow-up care for FN and non-FN patients transplanted for advanced liver disease was conducted. RESULTS: A total of 20 FN and 129 non-FN charts were available for review. FN subjects were younger at transplantation (mean [± SD] age 32.4±4.1 years versus 46.3±1.4 years; P=0.00005), less often male (35% versus 58%; P=0.05), more commonly transplanted for autoimmune hepatitis (30% versus 4.7%; P=0.006), less often from urban residences (25% versus 74%; P=0.0001) and less compliant with medical care (20% versus 80%; P=0.007). After a mean follow-up period of 11.0±1.5 years and 8.4±0.5 years in FN and non-FN subjects, respectively, the incidence and severity of rejection, graft and patient survival were similar between cohorts. CONCLUSION: Although demographic profiles, nature of the underlying disease and compliance differed, the rates and severity of rejection, graft and patient survival were similar in FN and non-FN patients who underwent liver transplantation for advanced liver disease.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".