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Record W4404474134 · doi:10.1002/jpn3.12415

Considerations in the development of the International Multicenter Pediatric Portal Hypertension Registry

2024· article· en· W4404474134 on OpenAlexaff
Tassos Grammatikopoulos, Catalina Jaramillo, Jean P. Molleston, Júlio Rocha Pimenta, O. Ackermann, Riccardo Superina, Roberto de Franchis, Serpil Tutan, Simon C. Ling, Uma Ramamurthy, Benjamin L. Shneider

Bibliographic record

VenueJournal of Pediatric Gastroenterology and Nutrition · 2024
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersTexas Children's Hospital
KeywordsMedicinePortal hypertensionMulticenter studyMEDLINEIntensive care medicinePediatricsInternal medicineCirrhosis

Abstract

fetched live from OpenAlex

Portal hypertension, a common sequela of chronic liver disease, is complicated by variceal hemorrhage, one of its most serious complications. Evidence-based approaches to managing variceal hemorrhage are limited by the scarcity of data related to this rare entity. Multicenter international registries are increasingly utilized to garner critical information about rare diseases. The International Multicenter Pediatric Portal Hypertension Registry (IMPPHR) was developed to acquire pediatric data about the mortality of first variceal hemorrhage and approaches to primary and second prophylaxis of variceal hemorrhage with a goal of improving outcomes in children with portal hypertension. IMPPHR evolved from pediatric portal hypertension symposia at the Baveno V and VI meetings in 2010 and 2015, with a formal executive committee initiating the development of IMPPHR in 2019. The registry opened in 2020, with data closure in 2024, including information from 44 centers and >700 subjects. The complexities and approaches to developing IMPPHR are described.

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)0.259
metaresearch head score (Gemma)0.248
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.259
Threshold uncertainty score0.914

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2590.248
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.004
Science and technology studies0.0020.002
Scholarly communication0.0110.013
Open science0.0070.009
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.250
Teacher spread0.234 · 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.

Study designNot applicable
Domainnot available
GenreMethods

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

Citations1
Published2024
Admission routes1
Has abstractyes

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