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Record W2752434507 · doi:10.1097/mpg.0000000000001721

Coagulopathy in Children With Liver Disease

2017· review· en· W2752434507 on OpenAlexaff
Patricia S. Kawada, Aisha Bruce, Patti Massicotte, Mary Bauman, Jason Yap

Bibliographic record

VenueJournal of Pediatric Gastroenterology and Nutrition · 2017
Typereview
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineThromboelastometryThromboelastographyCoagulopathyCoagulationLiver diseaseChronic liver diseaseAnticoagulantPopulationCoagulation testingHemostasisIntensive care medicineInternal medicineCirrhosisEnvironmental health

Abstract

fetched live from OpenAlex

It was thought that a high international normalized ratio predicted bleeding in patients with chronic liver disease (CLD) and patients were "autoanticoagulated." Contrary to this belief, while patients with CLD experienced bleeding, they also developed thromboses. In the last decade, the prevailing literature challenged the idea that an elevated international normalized ratio increased bleeding risk. The global assays of coagulation such as thromboelastography (TEG)/rotational thromboelastometry and thrombin generation assays provide additional insight into coagulation processes. It has become apparent that a parallel reduction of procoagulant and anticoagulant factors leave patients in a new "balanced" state, albeit a fragile one, where the balance can be easily disrupted. The inherent differences in coagulation between children and adults such as differences in levels of procoagulant and anticoagulant factors, underlying liver disease, and the paucity of studies in children make extrapolation of these findings to the pediatric population problematic. Ultimately, this is an area that requires further investigation to avoid inappropriate use of blood products and medication.

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)0.000
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.031
GPT teacher head0.303
Teacher spread0.271 · 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

Citations24
Published2017
Admission routes1
Has abstractyes

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