CD4 Count in HIV− Brain-Dead Donors
Bibliographic record
Abstract
BACKGROUND: The Human Immunodeficiency Virus (HIV) Organ Policy Equity Act allows for transplantation of organs from HIV-infected individuals (HIV+), provided it is performed under a research protocol. The safety assessment of an organ for transplantation is an essential element of the donation process. The risk for HIV-associated opportunistic infections increases as circulating CD4+ lymphocytes decrease to less than 200 cells/μL; however, the numbers of circulating CD4+ cells in the HIV-negative (HIV-) brain-dead donor (BDD) is not known. METHODS: Circulating T-lymphocyte subset profiles in conventional HIV- BDD were measured in 20 BDD in a clinical laboratory. RESULTS: The mean age of the BDD cohort was 48.7 years, 95% were white and 45% were women. The average body mass index was 29.2 kg/m. Cerebrovascular accident (40%) was the most prevalent cause of death. Sixteen (80%) subjects had a CD4 count ≤441 cells/μL (lower limit of normal) and 11 (55%) had a CD4 count less than 200 cells/μL; 11 (55%) subjects had a CD8 count ≤125 cells/μL (lower limit of normal). CD4/CD8 ratio was below normal in 3 patients (normal, 1.4-2.6). No recipient had a recognized donor-associated adverse event. CONCLUSIONS: Absolute numbers of CD4 and CD8 T-lymphocytes are commonly reduced after brain death in HIV- individuals. Thus, CD4 absolute numbers are an inconsistent metric for assessing organ donor risk, irrespective of HIV status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".