CADAVERIC ORGAN UTILIZATION IN SOUTHERN ALBERTA
Bibliographic record
Abstract
P98 Aims: Cadaveric organ donation is the main source of organs for transplantation. However, the number of organs obtained is not sufficient to meet the increasing demand. This need will not be covered from a cadaver source since the donor rate has not changed in North America for the past ten years and the brain death rate is, in fact, declining in Canada. To solve this shortage of organs, a renewed interest in living donation has increased the number of transplants. Another strategy is to extend the acceptance of organs for transplantation (marginal donors). This criteria varies according to the transplant centre and it cannot be measured. While donor rates are used widely as a measure of success in cadaveric donation, the efficiency of those donations to successfully provide actual organs for transplantation is seldom considered. Methods: From October 1997 to August 2003, all referrals for organ and tissue donation were analyzed in Southern Alberta and divided into groups: All Referrals, Organs, Tissue Referrals, Actual Organ Donors, and Donors Divided by Specific Organ Donation (Kidney, Heart, Liver, Pancreas, Lungs). We investigated the fate of these donations and grouped them as: Organs Transplanted, None Recovered, Discarded, and Used for Research. Results: There were 861 organ and tissue referrals, 232 (27%) were organ referrals and 167 (72%) became organ donors. Organ utilization was distributed as follows:Figure* Islet cells transplants Conclusions: By knowing organ utilization between regions, we can detect opportunities for allocation improvement since not all organs are transplanted at every procurement center. Potentially, we can increase the organ pool at the current cadaveric donor rate.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".