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Record W4226225395 · doi:10.3389/fped.2022.855162

Challenges in Management of VTE in Children With Cancer: Risk Factors and Treatment Options

2022· review· en· W4226225395 on OpenAlexaff
Nasrin Samji, Mihir D. Bhatt, Ketan Kulkarni

Bibliographic record

VenueFrontiers in Pediatrics · 2022
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsIzaak Walton Killam Health CentreDalhousie UniversityMcMaster University
Fundersnot available
KeywordsMedicineConcomitantIntensive care medicineThrombosisContext (archaeology)MalignancyAsymptomaticCancerPopulationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Venous thromboembolism (VTE) occurs in 2.1 to up to 50% of children with cancer and contributes to long term morbidity as well as early mortality in this population. Pediatric patients with malignancy are predisposed to VTE due to the prothrombotic nature of cancer and its associated coagulopathies as well as chemotherapeutic agents, use of central venous catheters, surgery, radiotherapy, and concomitant thrombophilia. Management of thrombosis in this population is challenging due to concomitant thrombocytopenia, associated bleeding risks, concurrent co-morbidities, and toxicities of therapy. The aim of this paper is to highlight clinically relevant issues and management dilemmas using clinical vignettes. We review the clinical significance of asymptomatic and symptomatic thrombosis, examine the various options for asparaginase-associated thrombosis, address the role and controversies of direct oral anticoagulants, and describe our approach to managing anticoagulation therapy in the context of chemotherapy-induced thrombocytopenia.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.049
GPT teacher head0.304
Teacher spread0.255 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

Explore more

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