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Record W2946346328 · doi:10.1097/tp.0000000000002785

Letermovir as Salvage Therapy for Cytomegalovirus Infection in Transplant Recipients

2019· article· en· W2946346328 on OpenAlexaff
Pakpoom Phoompoung, Victor H. Ferreira, Jussi Tikkanen, Shahid Husain, Auro Viswabandya, Deepali Kumar, Atul Humar

Bibliographic record

VenueTransplantation · 2019
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineViremiaViral loadAsymptomaticInternal medicineCytomegalovirusRefractory (planetary science)Adverse effectContext (archaeology)Hematopoietic stem cell transplantationImmunologyGastroenterologyTransplantationHerpesviridaeViral diseaseVirusBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Letermovir, a new viral terminase complex inhibitor, has been approved for the prevention of cytomegalovirus (CMV) infection in hematopoietic stem cell transplant patients. However, data on the efficacy and safety of letermovir for the treatment of CMV infection in transplant recipients remain scarce. METHODS: We performed a single-center retrospective study of stem cell and organ transplant recipients who received letermovir for the treatment of CMV infection from November 2017 to October 2018. RESULTS: Six patients were included, and 5 were evaluable. All received letermovir in the context of a refractory or resistant CMV infection including asymptomatic CMV viremia (n = 3), CMV syndrome (n = 1), and CMV pneumonitis and colitis (n = 1). The 3 asymptomatic patients experienced a decrease of the viral load (VL) to <200 IU/mL after letermovir therapy. One patient displayed a partial VL response (2-log of VL reduction) but a good clinical response, and one who received a suboptimal dose of letermovir experienced an increase of viremia. There were no treatment-related adverse effects. CONCLUSIONS: We demonstrate mixed efficacy in patients with refractory CMV infection suggesting that letermovir may be a useful therapeutic adjunct, potentially in combination with other antivirals.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.672

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)0.0000.000

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 head0.029
GPT teacher head0.330
Teacher spread0.301 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations51
Published2019
Admission routes1
Has abstractyes

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