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Record W4400195209 · doi:10.1080/10428194.2024.2371474

Cytomegalovirus infection risk with alemtuzumab therapy in hematological malignancies: a retrospective cohort study in the non-transplant setting

2024· article· en· W4400195209 on OpenAlexaff
Joel George, Lee Mozessohn, Philip W. Lam

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2024
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsAlemtuzumabMedicineRetrospective cohort studyCytomegalovirus infectionCohortInternal medicineCytomegalovirusImmunologyCohort studyOncologyHuman cytomegalovirusTransplantationViral diseaseHuman immunodeficiency virus (HIV)HerpesviridaeVirus

Abstract

fetched live from OpenAlex

Alemtuzumab is a potent lymphocyte-depleting immunotherapy used in solid organ transplan-tation (SOT), that is increasingly being applied in diverse lymphoproliferative disorders (LPDs). However, a significant toxicity limiting expanded usage is cytomegalovirus (CMV) infection, for which standardized preventive strategies exist in SOT but not in LPDs due to a poor understanding of infection risk in this population, with early LPD studies largely limited to stem cell transplantation. Using one of the most diverse arrays of LPDs studied to date, our retrospective cohort study of non-transplant patients receiving alemtuzumab over a ten-year period at a large regional cancer center examines the incidence and clinical profile of infected patients. Among 24 patients, we identified a composite CMV infection rate of 42% with a symptomatic rate of 21%. We also noted significant variations in preventive strategies, which alongside a high infection rate presents an opportunity to improve outcomes through further work in standardization.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.276
Teacher spread0.264 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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