Organ utilization from increased infectious risk donors: An observational study
Bibliographic record
Abstract
BACKGROUND: Donors with an increased risk of transmitting human immunodeficiency virus (HIV), hepatitis B virus (HBV), or hepatitis C virus (HCV) (increased risk donors [IRDs]) are a potential source of organs for transplant. Organs from IRDs can be utilized with appropriate recipient consent and post-transplant follow-up. We reviewed the characteristics and utilization of IRDs in our Organ Procurement Organization (OPO) over a 2-year period. METHODS: Donor information from April 1, 2013 to March 31, 2015 was obtained through the OPO database. Only consented donors were included. Donors were categorized as IRDs according to Health Canada/Canadian Standards Association (CSA) criteria. RESULTS: A total of 494 potential donors were identified, of which 92 (18.6%) were IRDs. Of these, at least one organ was transplanted from 76 (82.6%). Risk factors for IRDs included injection drug user (IDU) (12%), men having sex with men (MSM) (7%), commercial sex worker (CSW) (4%), and incarceration (24%). Fifty-nine percent (253/429) of IRD organs were utilized. The most frequently used organ was kidney, followed by liver. Median number of organs recovered per IRD was 3 (interquartile range: 2-5). Nucleic acid testing (NAT) was performed in 18.5% (17/92) of IRDs. Reasons for NAT were IDU (n = 2), MSM (n = 2), CSW (n = 2), and previous incarceration (n = 7). Organ utilization from donors that had NAT was similar to donors who did not (94% vs 80%, P = .29). Follow-up NAT was done in <5% of recipients from IRDs. CONCLUSIONS: In our cohort, IRDs comprised a significant proportion of donors. Utilization of IRD organs occurred at a significant rate regardless of pre-transplant NAT. These data suggest that multiple factors contribute to the perception of infectious risk from such organs.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".