MétaCan
Menu
Back to cohort
Record W2050354802 · doi:10.1055/s-2007-976176

Hemorrhagic Complications in Pediatric Hematologic Malignancies

2007· review· en· W2050354802 on OpenAlexafffund
Uma H. Athale, Anthony K.C. Chan

Bibliographic record

VenueSeminars in Thrombosis and Hemostasis · 2007
Typereview
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsMcMaster University
FundersHamilton Health SciencesHeart and Stroke Foundation of Canada
KeywordsMedicineAcute promyelocytic leukemiaMalignancyImmunophenotypingHematologic diseaseLeukemiaPlatelet transfusionPediatricsEpidemiologyHematologic malignancyIntensive care medicineComplicationBlood productDiseaseInternal medicineSurgeryImmunologyPlatelet

Abstract

fetched live from OpenAlex

Hematologic malignancies account for almost 40% of all cancers in children. Hemorrhage is the most common cause of early death in children with leukemia. Furthermore, major bleeding episodes lead to shorter survival and increased resource use. Potential risk factors for bleeding include hyperleukocytosis, immunophenotype of leukemia (especially acute promyelocytic leukemia), thrombocytopenia, and associated infections. Successful management of a bleeding episode is dependent on prompt identification of a child at high risk for bleeding, and should be directed at the replacement of blood products as well as to the aggressive therapy for underlying risk factors. Although in recent years there is a favorable decline in the hemorrhage-related mortality, the overall prevalence and the extent of morbidity posed by this potentially fatal complication, including disease outcome in children with hematologic malignancy, is largely unknown. In addition, there are no evidence-based guidelines for prophylactic blood or blood product transfusions. Prospective studies are required to define the epidemiology and risk factors predisposing children with hematologic malignancy to bleeding and to develop management guidelines.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.123
GPT teacher head0.412
Teacher spread0.288 · 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 teacher head, not a consensus.

Study designOther design
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

Citations34
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueSeminars in Thrombosis and HemostasisSame topicAcute Lymphoblastic Leukemia researchFrench-language works237,207