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Record W3127122736 · doi:10.1097/qai.0000000000002647

Cardiac Transplantation in HIV-Positive Patients: A Narrative Review

2021· review· en· W3127122736 on OpenAlexafffund
Faith Wairimu, Natalie C. Ward, Yingwei Liu, Girish Dwivedi

Bibliographic record

VenueJAIDS Journal of Acquired Immune Deficiency Syndromes · 2021
Typereview
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsContraindicationMedicineLife expectancyTransplantationAntiretroviral therapyDiseaseHuman immunodeficiency virus (HIV)Heart transplantationIncidence (geometry)Intensive care medicineOpportunistic infectionInternal medicineImmunologyViral loadViral diseasePopulationPathologyAlternative medicine

Abstract

fetched live from OpenAlex

ABSTRACT: Before the introduction of highly active antiretroviral therapy, patients infected with HIV experienced poor prognosis including high rates of opportunistic infections, rapid progression to AIDS, and significant mortality. Increased life expectancy after therapeutic improvements has led to an increase in other chronic diseases for these patients, including cardiovascular disease and, in particular, end-stage heart failure. Historically, HIV infection was deemed an absolute contraindication for transplantation. Since the development of highly active antiretroviral therapy, however, life expectancy for HIV-positive patients has significantly improved. In addition, there is a low incidence of opportunistic infections and the current antiretrovirals have an improved toxicity profile. Despite this, the current status of cardiac transplants in HIV-positive patients remains unclear. With this in mind, we conducted a narrative review on cardiac transplantation in patients with HIV.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.026
GPT teacher head0.349
Teacher spread0.323 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2021
Admission routes2
Has abstractyes

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Same venueJAIDS Journal of Acquired Immune Deficiency SyndromesSame topicHIV-related health complications and treatmentsFrench-language works237,207