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Clinical Utility of Cytomegalovirus Viral Load Testing for Predicting CMV Disease in D+/R- Solid Organ Transplant Recipients

2004· article· en· W2036501459 on OpenAlexaff
Atul Humar, Carlos V. Payá, Mark D. Pescovitz, Ed Dominguez, Kenneth Washburn, Emily A. Blumberg, Barbara D. Alexander, Richard B. Freeman, Nigel Heaton, Barbara M. Mueller

Bibliographic record

VenueAmerican Journal of Transplantation · 2004
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersF. Hoffmann-La Roche
KeywordsMedicineCytomegalovirusCytomegalovirus infectionViral loadSolid organOrgan transplantationImmunologyVirologyTransplantationHuman cytomegalovirusInternal medicineViral diseaseHerpesviridaeVirus

Abstract

fetched live from OpenAlex

Despite prophylaxis, cytomegalovirus (CMV) disease is common in donor seropositive (D+)/recipient seronegative (R-) transplant patients after cessation of prophylaxis. Early detection of CMV may allow for pre-emptive therapy to prevent active disease. The clinical utility of quantitative plasma viral load measurements for predicting CMV disease was determined in 364 D+/R- organ transplant patients receiving prophylaxis (100 d of valganciclovir or oral ganciclovir). Measurements were performed every 2 weeks until day 100 and at months 4, 4.5, 5, 6, 8 and 12 post-transplant. CMV disease occurred in 64 (17.6%) patients by 12 months. Using a positive cut-off value of >400 copies/mL, sensitivity was 38%, specificity 60%, positive predictive value 17%, and negative predictive value 82% for prediction of CMV disease. Therefore, routine monitoring would have predicted disease in only 24/64 (38%) patients. The test characteristics were not improved by changing the viral load cut-off point for defining a positive result. Similarly, single time point measures at the end of prophylaxis or month 4 had low sensitivity for disease prediction. Overall, regular CMV plasma viral load measurements were only of modest value in predicting CMV disease.

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)0.002
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 score0.066
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.045
GPT teacher head0.371
Teacher spread0.326 · 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

Citations110
Published2004
Admission routes1
Has abstractyes

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