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Record W2104324494 · doi:10.3171/ped.2004.101.2.0016

Nonsynostotic scaphocephaly: the so-called sticky sagittal suture

2004· article· en· W2104324494 on OpenAlexaff
James E. Baumgartner, Kelly Seymour-Dempsey, John F. Teichgraeber, James J. Xia, Amy L. Waller, Jaime Gateño

Bibliographic record

VenueJournal of Neurosurgery Pediatrics · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsMedicineSagittal sutureFibrous jointSagittal planeSurgeryAnatomyOrthodontics

Abstract

fetched live from OpenAlex

OBJECT: Scaphocephaly is a common craniofacial abnormality that results from craniosynostosis of the sagittal suture. The authors have treated a group of infants who presented with nonsynostotic scaphocephaly, or a so-called sticky sagittal suture. The purpose of this study was to describe these patients with nonsynostotic scaphocephaly, the natural history of the disease, and its treatment. METHODS: At the University of Texas-Houston Medical School between 1996 and 2002, nine patients presented with nonsynostotic scaphocephaly. When the abnormality in patients was diagnosed prior to 12 months of age, the majority (seven of eight) were successfully managed by molding helmet therapy. The only child in this group in whom this therapy failed to correct the deformity was noted on repeated computerized tomography scanning to have true sagittal synostosis, which required surgical correction. One child with nonsynostotic scaphocephaly, who presented after 1 year of age, required surgical correction followed by postoperative molding helmet therapy. CONCLUSIONS: Patients with nonsynostotic scaphocephaly appear to have sagittal synostosis. If treated early (at < 12 months of age), head shape in these patients can be normalized by molding helmet therapy. Surgery is reserved for older patients (> 12 months of age) or those with sagittal synostosis.

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 score0.204
Threshold uncertainty score0.484

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 studies0.0000.000
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 head0.227
Teacher spread0.220 · 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

Citations20
Published2004
Admission routes1
Has abstractyes

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