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Record W1997512595 · doi:10.2217/fnl.09.41

10th Asian & Oceanian Congress of Child Neurology: Expanding the Field of Child Neurology from the Region to the World

2009· article· en· W1997512595 on OpenAlexaboutno aff
Seong Hyun Kim, Heung Dong Kim

Bibliographic record

VenueFuture Neurology · 2009
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
Fundersnot available
KeywordsNeurologyPediatric NeurologyField (mathematics)MedicinePsychologyPediatricsPsychiatryMathematics

Abstract

fetched live from OpenAlex

The conference was organized by the Korean Child Neurology Society and included 102 lectures, presented by well-known experts in the field from 28 countries, and also platform and poster sessions.There were many interesting sessions covering a variety of topics in the field child neurology, as well as instructive teaching seminars.On the first day of the congress, 'Ketogenic Diet in Asian Countries' was introduced.The 'General Consensus' (J Helen Cross, UCL-Institute of Child Health, UK) and 'Future Direction' (Jong M Rho, St Joseph's Hospital, USA) were discussed, as well as 'How to Provide Ketogenic Diet in Asian Food Culture', which has useful practical applications.Moreover, various experiences in Asian countries including Korea, Iran, Taiwan and India were also presented.The presidential symposium on 'Genetic Implications in Childhood Neurological Disorders' took place on the second day; Ingrid Scheffer (University of Melbourne, Australia) presented 'Genetics of Epilepsy in Childhood: an Overview', focusing on the ion channel mutation.In the lecture on 'Mitochondrial Disorders: Diagnostic Challenge', David Thorburn (Mitochondrial & Metabolic Research, Australia) underlined that enzyme diagnosis using next-generation sequencing can be the starting point for investigation of the underlying molecular etiology.Ingrid Tein (University of Toronto, Ca nada) discussed the 'Approach to Neurometabolic Diseases in Children', which showed strategies according to symptoms and practical operational investigations.On the

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Commentary · Consensus signal: none
Teacher disagreement score0.418
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.268
Teacher spread0.258 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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
Published2009
Admission routes1
Has abstractyes

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