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Record W2572194573 · doi:10.1093/ecco-jcc/jjw217

Very Early Onset IBD: How Very Different ‘on Average’?

2017· editorial· en· W2572194573 on OpenAlexaff
Dan Turner, Aleixo M. Muise

Bibliographic record

VenueJournal of Crohn s and Colitis · 2017
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

There is a growing interest in the pathogenesis and epidemiology of very early onset IBD [VEOIBD], but the exact definition of VEOIBD has varied between publications. Whereas the original Montreal classification defined 16 years and younger as early onset IBD,1 the paediatric Paris modification further divided this age group into those aged 10–16 years and those under the age of 10 years2─while acknowledging that another subgroup of those 0–2 years of age [infantile IBD] may be characterised by a high rate of first-degree relatives and higher possibility of monogenetic aetiology. Since then, an increasing body of evidence suggests that although the likelihood of monogenetic aetiology is indeed highest among the youngest age group, monogenetic IBD may manifest in children up to 6 years of age and, in decreasing frequency, even during adulthood. Therefore, a more recent review suggested terming the entire 0–6 year age group as VEOIBD,3 but some used 0–6 and some 0–5 as the corresponding cutoff.4,5 Taken together it is reasonable to define the age group 7–16 years as early onset IBD [EOIBD] and 0–6 as VEOIBD, of whom 0–2 years should be termed ‘infantile IBD’. Nonetheless, the agreed classification scheme should be based on clinically relevant parameters, such as different phenotypes, aetiology, or prognosis.

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)0.010
metaresearch head score (Gemma)0.038
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.020
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.038
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0040.002
Science and technology studies0.0030.003
Scholarly communication0.0080.006
Open science0.0040.003
Research integrity0.0200.031
Insufficient payload (model declined to judge)0.0070.006

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 head0.006
GPT teacher head0.236
Teacher spread0.230 · 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
GenreEditorial

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

Citations17
Published2017
Admission routes1
Has abstractno

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