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Record W2944277415 · doi:10.21693/1933-088x-10.2.117

Using Advanced Pulmonary Hypertension Therapies in Children

2011· article· en· W2944277415 on OpenAlexaboutno aff
Erika B. Rosenzweig, D. Dunbar Ivy, Maurice Beghetti, Jeffrey A. Feinstein

Bibliographic record

VenueAdvances in Pulmonary Hypertension · 2011
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsConversationMedicineUniversity hospitalAssociate editorPediatricsFamily medicineMedical educationPsychologyLibrary science

Abstract

fetched live from OpenAlex

On July 8, 2011, Editor-in-Chief Erika Rosenzweig convened a discussion among a group of physicians who work daily with pediatric PH patients to share their thoughts and experiences related to using new therapies with children. Contributing to the conversation were guest editor Dunbar Ivy, MD, Professor of Pediatrics, University of Colorado; Jeffrey A. Feinstein, MD, MPH, Associate Professor, Stanford University School of Medicine; Tilman Humpl, MD, PhD, Associate Professor, Pediatrics, University of Toronto; and Professor Maurice Beghetti, Head of Pediatric Subspecialties Division and Head of Pediatric Cardiology Unit, Children's University Hospital, Geneva, Switzerland.

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)0.005
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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
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 head0.051
GPT teacher head0.301
Teacher spread0.250 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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