The First 2 Years of Activity of a Specialized Organ Procurement Center: Report of an Innovative Approach to Improve Organ Donation
Bibliographic record
Abstract
The number of patients requiring organ transplants continues to outgrow the number of organs donated each year. In an attempt to improve the organ donation process and increase the number of organs available, we created a specialized multidisciplinary team within a specialized organ procurement center (OPC) with dedicated intensive care unit (ICU) beds and operating rooms. The OPC was staffed with ICU nurses, operating room nurses, organ donor management ICU physicians, and multidisciplinary staff. All organ donors within a designated geographic area were transferred to and managed within the OPC. During the first 2 years of operation, 126 patients were referred to the OPC. The OPC was in use for a total of 3527 h and involved 253 health workers. We retrieved 173 kidneys, 95 lungs, 68 livers, 37 hearts, and 13 pancreases for a total of 386 organs offered for transplantation. This translates to a total of 124.6 persons transplanted per million population, which compares most favorably to recently published numbers in developed countries. The OPC clearly demonstrates potential to increase the number of deceased donor organs available for transplant. Further studies are warranted to better understand the exact influence of the different components of the OPC on organ procurement.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".