Bibliographic record
Abstract
PURPOSE: The improvements in renal transplantation over the last 10 years have been one of the great success stories in medicine. We reviewed these successes with a focus on the following: changes in demographics of donors and recipients in Canada, the benefits of new immunosuppressive regimes and the efforts to minimize their toxicity and finally, our understanding of measures to circumvent chronic rejection. MATERIALS AND METHODS: A review of current transplantation literature was performed and pertinent data presented. As well, information from the Canadian Organ Replacement Register was selected to provide an overview of changes in renal transplantation in Canada. RESULTS: Despite the stable rate of transplantation in Canada, the number of new patients starting dialysis each year roughly equals the entire national renal transplant waiting list. These patients are older and have more complex co-morbidities mandating prudent use of immunosuppression so as to minimize toxicity. Standard triple therapy consists of a calcineurin inhibitor, an antimetabolite and corticosteroids. Antibody therapy is indicated in sensitized recipients and newer monoclonal humanized antibodies offer less toxicity. Nonspecific therapies are less favorable due to unwanted side effects and we can now identify subsets of patients who are most likely to benefit from specific therapy. Newer non-nephrotoxic agents hold promise for future regimens. However a paucity of large, multicenter, randomized trials, tested against standard protocols, limits their current indications. Many immunologic and non-immunologic factors influence the outcome of renal transplantation and play a role in the development in acute and chronic rejection. CONCLUSIONS: The challenges of renal transplantation over the next 10 years are: 1) in the development of specific therapies that can be altered according to patient co-morbidities and other factors influencing outcome; 2) minimizing toxicity; 3) preventing chronic rejection; and 4) improving our national organ donation network.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".