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Record W2525478590 · doi:10.1097/tp.0000000000001506

CD4 Count in HIV− Brain-Dead Donors

2016· article· en· W2525478590 on OpenAlexaff
Oscar K. Serrano, Scott Kerwin, William D. Payne, Timothy L. Pruett

Bibliographic record

VenueTransplantation · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsCegep de Sept Iles
Fundersnot available
KeywordsBrain deadHuman immunodeficiency virus (HIV)MedicineVirologyInternal medicineTransplantation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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.

Opus teacher head0.012
GPT teacher head0.257
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

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