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Record W4414168577 · doi:10.1002/pbc.32032

Nirsevimab for Prevention of RSV Infections for Immunocompromised Children With Cancer and Stem Cell Transplant Recipients: A Single‐Center Experience

2025· article· en· W4414168577 on OpenAlexaff
Adam P. Yan, Yuqing Feng, Dima El Hassanieh, Yi Man, Tal Schechter‐Finkelstein, Jennifer Drynan, Lillian Sung, Sumit Gupta

Bibliographic record

VenuePediatric Blood & Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsCancerStem cellHematopoietic stem cell transplantationHematopoietic stem cellRespiratory systemVirusMonoclonal antibodyHuman immunodeficiency virus (HIV)Transplantation

Abstract

fetched live from OpenAlex

Children with cancer and hematopoietic stem cell transplant (HSCT) recipients are at high risk for severe respiratory syncytial virus (RSV) infections. Nirsevimab, a long-acting monoclonal antibody approved in 2023, offers single-dose seasonal protection. We conducted a single-center study to assess uptake, factors associated with receipt, and RSV outcomes among eligible patients during the 2024-2025 season. Of 42 eligible patients, 62% received nirsevimab. Delays and missed opportunities for administration were common. RSV occurred in both recipients and non-recipients, including one RSV-related death in an unvaccinated patient. Clinic type and healthcare contact influenced uptake. These findings highlight the need for improved implementation strategies to optimize RSV prophylaxis in immunocompromised pediatric populations.

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.026
GPT teacher head0.337
Teacher spread0.311 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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