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Record W1968557373 · doi:10.1002/ajmg.a.31326

Osteocraniostenosis–hypomineralized skull with gracile long bones and splenic hypoplasia. Four new cases with distinctive chondro‐osseous morphology

2006· article· en· W1968557373 on OpenAlexaff
Alison M. Elliott, William R. Wilcox, Gerald S. Spear, Fiona M. Field, Thora S. Steffensen, Barbara D. Friedman, David L. Rimoin, Ralph S. Lachman

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsUniversity of Manitoba
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Research Resources
KeywordsSkullAnatomyHypoplasiaMedicine

Abstract

fetched live from OpenAlex

Osteocraniostenosis is a severe skeletal dysplasia characterized by a hypomineralized skull that has been previously described as kleeblattschädel (cloverleaf skull) and overtubulated long bones. Dysmorphic facial features include a short nose, short philtrum, and a small, inverted V-shaped mouth. Splenic a/hypoplasia is a constant finding. We report four infants (two unrelated and two siblings) with osteocraniostenosis and describe the clinical, radiographic and chondro-osseous morphology findings. The two siblings lack the moderate long-bone shortening that is typically seen. The skull configuration is likely caused by severely hypoplastic cranial bones (parietal) rather than true craniosynostosis, making the term "osteocraniostenosis" misleading. Histological examination of bone in all cases showed an abnormal growth plate with short irregular columns. The resting cartilage showed pleomorphic chondrocytes with increased cellularity and unique pseudocolumn formation. There are some radiographic and chondro-osseous morphologic similarities between osteocraniostenosis and severe Hallermann-Streiff syndrome (HSS), suggesting the two disorders may be pathogenetically related.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.234
Teacher spread0.227 · 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 designObservational
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

Citations11
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Medical Genetics Part ASame topicCraniofacial Disorders and TreatmentsFrench-language works237,207