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Record W3209563740 · doi:10.3390/v13112146

Respiratory Viruses in Solid Organ Transplant Recipients

2021· review· en· W3209563740 on OpenAlexaff
Roni Bitterman, Deepali Kumar

Bibliographic record

VenueViruses · 2021
Typereview
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineImmunologyPopulationRespiratory systemTransplantationImmunogenicityIntensive care medicineVirologyInternal medicineImmune system

Abstract

fetched live from OpenAlex

Solid organ transplantation is often lifesaving, but does carry an increased risk of infection. Respiratory viral infections are one of the most prevalent infections, and are a cause of significant morbidity and mortality, especially among lung transplant recipients. There is also data to suggest an association with acute rejection and chronic lung allograft dysfunction in lung transplant recipients. Respiratory viral infections can appear at any time post-transplant and are usually acquired in the community. All respiratory viral infections share similar clinical manifestations and are all currently diagnosed using nucleic acid testing. Influenza has good treatment options and prevention strategies, although these are hampered by resistance to neuraminidase inhibitors and lower vaccine immunogenicity in the transplant population. Other respiratory viruses, unfortunately, have limited treatments and preventive methods. This review summarizes the epidemiology, clinical manifestations, therapies and preventive measures for clinically significant RNA and DNA respiratory viruses, with the exception of SARS-CoV-2. This area is fast evolving and hopefully the coming decades will bring us new antivirals, immunologic treatments and vaccines.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.273
GPT teacher head0.490
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations13
Published2021
Admission routes1
Has abstractyes

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