Poor Socio-Economic-Status Does Not Negatively Affect Outcomes of Liver Transplant Listings.
Bibliographic record
Abstract
Poor socio-economic status (SES) has been associated with higher mortality while on the wait list, and worse outcomes and survival in liver transplantation. The effect of SES within the Canadian universal health care coverage remains unclear. We evaluated the effect of SES using census information derived from candidate postal codes. METHODS: The association between listing outcomes and SES was examined in a multivariate logistic-regression model. RESULTS: Between 2000 and 2010 2739 liver candidates were listed at our institution. Mean age was 52±10.5; 67.9% were male; mean income was $43078, median wait list time for transplant was 195(0-4171) with 17.5% death on wait list. A total of 58.3% of these candidates were transplanted. Income was not associated with improved access to liver transplantation after listing at our centre OR 1.002 (0.87-1.15), nor with increased mortality while on the wait-list OR 0.97 (0.80-1.16). However, we did observe a slightly shorter wait time in the higher income groups (201 vs. 174, p=0.005) partially due to access to live donation in women. Conclusions: Higher income individuals may experience shorter liver wait times; however, poor socio-economic status does not have a negative impact on listing outcomes or mortality in a universal health care coverage setting.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".