Exception points for liver transplantation: A Canadian review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Exception points for liver transplant (LT) allocation are used to account for mortality risk not reflected by scoring systems such as the Model for End-Stage Liver Disease with sodium (MELD-Na). Currently, there is no formal policy regarding exception points in Canada, and differences across the country are not well understood. As such, a review of the criteria and exception points granted throughout the country for LT was conducted. Methods: Seven LT centres in five provinces were surveyed (Vancouver, Edmonton, London, Toronto, Montréal, Halifax) regarding the indications and criteria for exception points granted, the number of points granted, how points would be accrued, and the maximum points granted. Results: Programs in British Columbia and Nova Scotia grant variable exception points based on the median MELD-Na score with modifications; Alberta, Ontario, and Quebec grant exception points using specific values based on the indication. Overall, there was significant heterogeneity regarding exception points granted nationally with agreement only for awarding exception points for hepatopulmonary syndrome and polycystic liver disease. The second most common agreed-upon indications for exception points were portopulmonary hypertension and recurrent cholangitis offered by four provinces. Quebec had the most formal criteria for non-cirrhosis-based conditions. Conclusions: There is substantial variance across the country regarding the indications for granting exception points as well as the number of points granted. Future work on developing a national consensus will be important for the development of equity in LT across Canada.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 it