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Record W4396791552 · doi:10.1111/tid.14292

Cytomegalovirus surveillance after antiviral prophylaxis in CMV mismatched transplant patients: Does recurrent cytomegalovirus DNAemia impact patient survival?

2024· article· en· W4396791552 on OpenAlexaff
Oscar A. Fernández‐García, Cristina Hernandez, Mark L. Robbins, Dima Kabbani, Karen Doucette, Carlos Cervera

Bibliographic record

VenueTransplant Infectious Disease · 2024
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineCytomegalovirusIncidence (geometry)CohortPopulationRetrospective cohort studyCumulative incidenceInternal medicineHuman cytomegalovirusDiseaseViral loadImmunologyViral diseaseHerpesviridaeVirus

Abstract

fetched live from OpenAlex

BACKGROUND: Cytomegalovirus (CMV) mismatched, donor IgG-positive/recipient IgG-negative, solid organ transplant recipients (SOTRs) are at high risk of CMV invasive disease. Post-prophylaxis disease is an issue in this population. Some programs employ surveillance after prophylaxis (SAP) to limit the incidence of post-prophylaxis disease. METHODS: This was a single-center retrospective cohort study that included all CMV mismatched SOTRs from 2003 to 2017. Patients underwent SAP with weekly CMV plasma viral load for 12 weeks. The subjects were classified into three post-prophylaxis DNAemia patterns: no DNAemia, one episode of DNAemia, and multiple episodes of DNAemia. We calculated the cumulative incidence of each DNAemia pattern. We also determined 5-year mortality based on DNAemia pattern stratified by organ transplant type. RESULTS: Post-prophylaxis recurrent DNAemia occurred in 63% of lung recipients and 32% of non-lung recipients (p = .003). Tissue invasive CMV disease was diagnosed in 3% of the population and CMV syndrome was diagnosed in 33%. Recurrent DNAemia was not associated with 5-year mortality. CONCLUSION: In this cohort, undergoing SAP tissue invasive disease was uncommon and CMV DNAemia recurrence did not have an impact on long-term mortality.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.278
Teacher spread0.268 · 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 designObservational
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

Citations3
Published2024
Admission routes1
Has abstractyes

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