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Record W2018948986 · doi:10.2310/7070.2001.20054

Comparison of Craniofacial Skeletal Characteristics of Infants with Bilateral Choanal Atresia and an Age-Matched Normative Population: Computed Tomography Analysis

2001· article· en· W2018948986 on OpenAlexaffvenue
Roy T.H. Cheung, Mark E. Prince

Bibliographic record

VenueThe Journal of Otolaryngology · 2001
Typearticle
Languageen
FieldMedicine
TopicCongenital Ear and Nasal Anomalies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineCraniofacialChoanal atresiaComputed tomographicAtresiaSkullComputed tomographyCraniofacial abnormalityDentistryOrthodonticsSurgery

Abstract

fetched live from OpenAlex

Choanal atresia (CA) results from the developmental failure of the posterior nasal cavity to communicate with the nasopharynx. Computed tomographic (CT) scanning is often used as a diagnostic tool for CA as it is able to provide information regarding the extent and type of atresia. Studies have used CT measurements to analyze the skeletal deformities of children with CA. Computed tomographic analysis of the complete craniofacial skeletal characteristics of children with CA has not been previously reported. This study analyzed the craniofacial skeletal characteristics of infants with bilateral choanal atresia (BCA) and compared them with age-matched standards. Eight patients with BCA under the age of 3 months were evaluated. Fourteen cranio-orbitozygomatic variables were used to represent the craniofacial skeletal configuration. The measurements from the control group were compared with the available values of age-matched normal controls. Statistically significant differences between the means of the sample group and control group were demonstrated in 10 of 14 variables. The sample group means were consistently smaller than the control group mean. Detailed knowledge of the underlying anatomy of infants with BCA will help in the development of treatment strategies and will provide data for evaluation of operative intervention on craniofacial growth.

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 score0.002
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.296
Teacher spread0.281 · 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

Citations15
Published2001
Admission routes2
Has abstractyes

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