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Record W3088526011 · doi:10.1002/pbc.28734

Platelet transfusion practice and platelet refractoriness for a cohort of pediatric oncology patients: A single‐center study

2020· article· en· W3088526011 on OpenAlexafffund
Lani Lieberman, Yang Liu, Rebecca Barty, Nancy M. Heddle

Bibliographic record

VenuePediatric Blood & Cancer · 2020
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsMcMaster UniversityUniversity Health NetworkUniversity of TorontoMcMaster University Medical CentreHealth Sciences CentreSunnybrook Health Science Centre
FundersCanadian Blood Services
KeywordsMedicinePlateletPlatelet transfusionInterquartile rangeSingle CenterDemographicsInternal medicinePediatricsMalignancyCohort

Abstract

fetched live from OpenAlex

BACKGROUND: Platelet transfusions are an essential aspect of supportive care for pediatric oncology patients. Data regarding the frequency of transfusions, pretransfusion thresholds, posttransfusion increments, and rate of platelet transfusion refractoriness (PTR) are lacking. STUDY OBJECTIVES: (a) describe platelet transfusion practice for children with malignancy; (b) determine the normal platelet increment following platelet transfusion; and (c) assess rate of PTR. METHODS: Inpatient pediatric oncology patients <18 years old and treated between 2009 and 2013 were identified. Data collected retrospectively included patient demographics, clinical information, laboratory values, and transfusion details. RESULTS: /L). The rate of PTR (immune and/or nonimmune) was low (8%; 11/144). CONCLUSIONS: Practical information to parents and clinicians of newly diagnosed children regarding the likelihood and frequency of platelet transfusions was determined. The rate of PTR was low, supporting the hypothesis that children receiving leukoreduced products are at a low risk of PTR.

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)0.003
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.302
Teacher spread0.277 · 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

Citations8
Published2020
Admission routes2
Has abstractyes

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