MétaCan
Menu
Back to cohort
Record W2109753113 · doi:10.1002/0471695998.mgs010

<scp>B</scp>eckwith‐<scp>W</scp>iedemann Syndrome and Hemihyperplasia

2005· other· en· W2109753113 on OpenAlexaff
Rosanna Weksberg, Cheryl Shuman

Bibliographic record

Venuenot available
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Syndromes and Imprinting
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMacroglossiaBeckwith–Wiedemann syndromeMedicineDuodenal atresiaPolyhydramniosPopulationIncidence (geometry)PediatricsEndocrinologyInternal medicineBiologyGeneticsPathologyPregnancyFetusGeneTongueAtresia

Abstract

fetched live from OpenAlex

Abstract Beckwith‐Wiedemann syndrome is characterized by a triad of exomphalos, macroglossia, and gigantism. The population incidence is estimated to be 1 in 13,700 with equal incidence in males and females. This is likely an underestimate as cases with milder phenotypes may not be diagnosed. Some cases of isolated hemihyperplasia may, in fact, represent cases of Beckwith‐Wiedemann syndrome with reduced expressivity. Clinical features of Beckwith‐Wiedemann syndrome, in addition to the triad mentioned above, include hemihyperplasia, umbilical hernia, diastasis recti, embryonal tumors, adrenocortical cytomegaly, ear anomalies, visceromegaly, renal abnormalities, and neonatal hypoglycemia. Supportive findings may include polyhydramnios and prematurity, enlarged placenta, cardiomegaly, and characteristic facies. The latter feature is much more recognizable in early life and becomes less obvious over time. Beckwith‐Wiedemann syndrome is a complex multigenic disorder caused by alterations in growth regulatory genes on chromosome 11p15 and can currently be categorized into eight distinct genetic groups.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.002

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 head0.220
Teacher spread0.214 · 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 designCase report
Domainnot available
GenreEmpirical

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

Citations8
Published2005
Admission routes1
Has abstractyes

Explore more

Same topicGenetic Syndromes and ImprintingFrench-language works237,207