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Record W4384627157 · doi:10.1111/tid.14101

Updates in hematopoietic cell transplant and cellular therapies that enhance the risk for opportunistic infections

2023· review· en· W4384627157 on OpenAlexaff
Geoff D.E. Cuvelier, Kristjan Paulson, Eric J. Bow

Bibliographic record

VenueTransplant Infectious Disease · 2023
Typereview
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsMedicineInfectious disease (medical specialty)DiseaseHematopoietic cellImmunologyIntensive care medicineChimeric antigen receptorImmune systemCell therapyTransplantationHaematopoiesisStem cellImmunotherapyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Infectious disease physicians may be asked to evaluate and manage a variety of infections in immunocompromised hosts undergoing hematopoietic cell transplant (HCT) and cellular therapies. Over the last decade, several advances in cellular therapy have occurred, with implications for the types of infectious complications that may be seen. AIMS: The purpose of this review is to update the infectious disease physician on newer advances in HCT and cellular therapy, including haploidentical transplant, expanding indications for transplant in older individuals and children, and chimeric antigen receptor T-cells. We will review how these advances might influence infectious disease complications following HCT. We will also provide a perspective that infectious disease physicians can use to evaluate the degree of immune suppression in an individual patient to help determine the type of infections that may be encountered.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.042
GPT teacher head0.308
Teacher spread0.266 · 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 designNot applicable
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

Citations3
Published2023
Admission routes1
Has abstractyes

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